Global-scale measurements of air-sea variables and associated boundary layer processes are crucial for determining ocean surface fluxes, understanding atmosphere-ocean interactions, validating remotely sensed data, and enhancing coupled model simulations. Traditional observation platforms like ships and moored buoys face limitations in capturing the spatial-temporal variabilities of air-sea interactions globally. Drifting and autonomous surface vehicles have emerged as promising complements for the air-sea interface observation. We introduce the drifting air-sea interface buoy (DrIB), a low-cost minibuoy designed to measure essential climate and ocean variables at the air-sea interface in a free-drifting way, while ensuring the stability of the buoy's attitude. It is capable of surface seawater and 3-m meteorological observations, including sea surface temperature, air pressure, temperature, humidity, and vector wind speed. By the end of 2022, over 74 DrIBs were tentatively deployed in the regions of the Kuroshio Extension, western Pacific, South China Sea, and Southern Ocean. Comparative data analyses with a moored buoy [Kuroshio Extension Observatory (KEO), an Ocean Climate Station operated by NOAA in the Kuroshio Extension] and ship-borne measurements prove the feasibility of DrIB's observation at the air-sea interface. The DrIB-KEO comparison experiment demonstrates statistical consistency across observation parameters (wind speed, air temperature, relative humidity, air pressure, and sea surface temperature), with correlation coefficients exceeding 0.95. DrIB demonstrates promising potential in delivering global air-sea interface variables and turbulent heat fluxes. Future regional and global deployments of DrIBs will enhance satellite remote sensing data validation and improve the study of meso-/frontal-scale air-sea interactions, advancing ocean-atmosphere coupled model simulations.

It is generally recognised that the north-easterly monsoon leads to greater intrusion of Changjiang Diluted Water (CDW) into Hangzhou Bay in winter than in summer, which strongly influences the hydrography of Hangzhou Bay. However, anomalously lower salinity (<10) and higher dissolved inorganic nitrogen (DIN) concentrations (1.9 mg L- 1) were observed in central Hangzhou Bay in summer (August 2019) than the salinity (>15) and DIN concentrations (1.5 mg L- 1) in winter (March 2022). A high-resolution, well-validated model has further revealed intermittent intrusions of CDW, characterised as intraseasonal (30-120 d) and episodic variations, resulting in a unique low-salinity and high-nutrient water mass in the north-central bay during summer. The intermittent intrusions are driven by the combined effects of the Changjiang discharge, intraseasonal winds, tropical cyclones, and tidal residual currents. The Changjiang discharge amplifies the effect of CDW intrusion on a seasonal scale, which is responsible for the large (>10) decline in summer salinity in the initial intrusion area of the northern bay mouth. The intraseasonal variation in the south-westerly monsoon in summer causes intermittent intrusion of the CDW, accompanied by variations in the Taiwan Warm Current. Intrusion of CDW is greatly enhanced during episodic events of strong northerly winds caused by tropical cyclones. This episodic intrusion can cause a decline in salinity in the initial intrusion area of up to 6 in approximately one week. After the CDW intrudes into the north central bay, the westward tidal residual current along the northern coast continuously transports the CDW toward the head of Hangzhou Bay.

The degradation of coastal seawater quality off the Changjiang Estuary and adjacent waters is typically associated with monsoon wind, ocean currents and inputs of terrestrial pollutants. In addition to these factors, the passage of typhoons can be also important in driving short-term fluctuations in coastal water quality. Using a coupled Regional Ocean Modeling Systems (ROMS) and carbon, silicate, and nitrogen ecosystem (CoSiNE) model with Eulerian tracers, we investigated the transport processes of pollutants discharged from the Changjiang during the passage of Typhoons Lingling and Tapah (2019). The model results show that Typhoons Lingling and Tapah significantly enhanced the southward transport of these pollutants, leading to a sudden and noteworthy degradation of water quality in the Zhejiang coastal region during early autumn, despite no considerable change in monsoon pattern or river discharge compared to normal years. As Lingling and Tapah successively passed through the East China Sea in September, the average nitrate concentration in the Zhejiang coastal waters rose by 77%, and the percentage of heavily polluted water increased by 28%. Our numerical experiments showed that the impacts caused by Lingling and Tapah on nitrate levels in these waters lasted for approximately 34 and 23 days, respectively. These results indicate that typhoons play a crucial role in regulating the transport of pollutants in coastal waters, with significant sub-seasonal effects on the marine biogeochemical environment.

Using in situ observations collected by a drifting air–sea interface buoy (DrIB) in the northern South China Sea from August 30 to September 13, 2018, the extreme air–sea turbulent fluxes that occurred from September 8 to 13 during tropical cyclone (TC) Barijat were investigated. The most striking features were substantial increases in momentum and heat fluxes, with maximum increases of 10.8 m s−1 in the wind speed (WS), 0.73 N m−2 in the wind stress, 68.1 W m−2 in the sensible heat fluxes (SH) and 258.8 W m−2 in the latent heat fluxes (LH). The maximum WS, wind stress, SH and LH values amounted to 15.3 m s−1, 0.8 N m−2, 70.9 W m−2 and 329.9 W m−2, respectively. Using these new DrIB observations, the performance of two state-of-the-art, high-resolution reanalysis products, ERA5 and MERRA2, was assessed. The consistency of the observed values with ERA5 was slightly better than with MERRA2, reflected in higher correlations but both products underestimated the WS during TC conditions. In calm weather conditions, the turbulent heat fluxes were overestimated, because they simulated a too dry and cold atmospheric state, enhancing the air–sea differences in temperature and humidity. Considering that an accurate representation of the air–sea turbulent and momentum fluxes is essential for understanding and predicting ocean and atmospheric variability, our findings indicate that more high-quality temperature and relative humidity observations are required to evaluate and improve existing reanalysis products.

Accurate wind speed prediction is significant to maintain the stable power system operation and enhance wind power utilization efficiency. However, most of wind speed prediction models cannot well fit the variation pattern of wind series due to its random and chaotic characteristics. This study proposes a novel wind speed prediction model incorporating fractal dimension (FD) and variational mode decomposition (VMD) and general continued fraction (GCF). More specifically, the fractal feature of wind speed series is analyzed, and the parameter determination strategy for VMD based on fractal feature of wind series is developed to overcome the problem of ambiguous mode parameter K for VMD, so that the relatively regular modes can be extracted from the raw wind series. To well capture the variation characteristics of wind series, a novel GCF model is derived on the basis of the inverse difference quotient theory, and the structure parameters of GCF model are determined by the bats algorithm (BA). To verify the accuracy and stability of the proposed model, several experiments involving comparisons with some mainstream models are executed on five wind speed datasets, and the proposed model outperforms comparative models with mean absolute percentage error reduction of 0.48 %-74.44 %. The results of the experiments indicate the proposed model has great capacity of capturing the variation characteristics of wind speed series.

To better capture the variation characteristics of short-term power load, a power load prediction method based on High Frequency Empirical Mode Decomposition (HFEMD) and Long Short Memory network optimized by Genetic Algorithm (GALSTM) is proposed. For non-stationary characteristics of short-term power load, HFEMD is employed to decompose the load sequence in a high precision, and the selection to frequency and amplitude of high frequency inharmonic signal for HFEMD is researched. Then GA is adopted to optimize the structure parameters of LSTM model. The experiments of one-step and multi-step prediction on actual load data in some area in US is executed. HFEMD can effectively improve the mode mixing problem of EMD and avoid the unnecessary components of EEMD caused by assisted white noise. Meanwhile, the proposed model can better excavate the variation characteristics of power load and enhance the forecasting accuracy for short term power load.

针对绞吸式挖泥船吸扬系统匹配选型不佳和产量预测不准等问题,利用数字仿真建模技术重构绞吸挖泥船吸扬系统的镜像数字模型.针对不同的绞吸挖泥船,通过调整模型参数可求解得到与之相对应的各子系统数字模型.在绞刀及其驱动系统中定量分析了绞刀转速、横移速度和吸口流速与泥浆比重的关系;在泥泵与管路系统中根据相似定律构建了泥泵-管路模型,研究了不同土质和泥浆浓度对泥泵扬程的影响,并求解预测出不同管道流速下的施工最佳工况点.将仿真系统应用到实验室小型疏浚平台上进行试验验证,试验结果显示数字系统能准确预测施工动态参数和产量,并能提前为施工策略提供指导性建议.

The short-term power load forecasting is the understructure for energy optimization management of power system and coordinated scheduling of power resources. It is difficult to predict accurately due to the high randomness and periodicity of short-term load. To solve the shortcomings, the short-term forecasting method is proposed based on Ensemble Empirical Mode Decomposition-Adaptive Boosting Gated Recurrent Unit Neural Network (EEMD-ABGRU). First, the stable components are obtained by processing the nonstationary original series with EEMD, and then each component forecasting model is established by Gated Recurrent Unit neural network(GRU). Finally, Adaptive Boosting algorithm (Adaboost) is integrated with GRU neural network and the integrated model can extract the complex characteristics of load data more accurately, identify the law of load sequence variation and achieve high precision component forecasting. Taking actual load data of power grid as practical examples, the forecasting accuracy of proposed method reaches 97.18% which is higher than that of Long Short Term Memory Neural Network (LSTM) method. And the efficiency and adaptability of the algorithm is verified on different quarterly datasets.

青海省夏日哈-什多龙成矿远景区位于青藏高原东昆仑东段,西起都兰县夏日哈镇,东至兴海县什多龙一带,跨祁漫塔格北坡-夏日哈岩浆弧和北昆仑岩浆弧,是青海省重要的成矿区.为了在该区寻找新的成矿有利地段,本文在对该区区域地质背景、矿产特征及区内典型矿床地质特征系统研究的基础上,利用多元素衬值累加地球化学找矿方法,在区域上优选出一批找矿靶区.多元素衬值累加地球化学找矿方法首先收集远景区1:5万水系沉积物测量成果,经过数据处理,绘制远景区衬值累计地球化学图,进而建立典型矿床找矿模式,最终通过对比分析,选出新的找矿靶区.对圈定的找矿靶区进行野外踏勘检查,均发现不同程度的矿化线索,为今后在该区进一步找矿起到了很好的指导作用.通过本次工作,发现了该方法在靶区圈定方面的优势:(1)可快速圈定异常,提高工作效率;(2)编制的地球化学图浓集中心更显著;(3)圈定的找矿靶区找矿成果显著,尤其针对陆相火山岩型多金属矿、斑岩型铜钼矿、矽卡岩型多金属矿找矿成果突出;(4)能直观反映背景变化趋势,有助于评价异常.

为减少轮班制度下地铁司机的疲劳风险,基于司机轮班疲劳的5个评价指标,研究适用于轮班疲劳评价的池田公式和疲劳指数(FI)等2种方法.对比2种方法的理论基础及计算模型,并运用这2种方法计算北京燕房线地铁司机的轮班疲劳度.结果 表明:2种方法在评估地铁司机单班疲劳时给出的结果存在明显差异,在评估周期轮班疲劳时结果显著相关;用池田公式可精确评定轮班表中的每项工作引起的疲劳,用FI法评价长时间轮班疲劳时更为简单便捷.

该文针对电阻点焊超声检测熔核直径评估算法中,检测信号各波峰幅值不同程度衰减对熔核直径评估结果的影响,提出一种基于小波变换超声信号增益补偿方法,将原信号进行3层小波分解获得4个子带信号,分别计算各子带信号衰减系数并补偿,再通过小波重构得到重构信号,最后利用重构信号计算熔核直径.通过构建点焊超声检测有限元仿真模型,研究信号衰减对熔核直径计算结果的影响,为试验提供理论依据.运用该方法处理多组点焊超声检测数据,对比分析处理前后熔核直径计算值与金相实测值,研究结果表明,采用该方法处理超声信号并计算点焊熔核直径,误差减小到0.1 mm,可有效提高评估精度.

为了研究高速动车组作为大功率单相非线性负荷引起的电力系统三相电流不平衡问题,通过对V/v接线牵引变压器负序电流的分析,引入随机过程,建立了牵引变电所负荷和负序电流概率模型.利用统计推断解析和模拟负序电流的概率分布,并进行了仿真分析,得出了V/v接线方式高速铁路牵引供电系统的负序电流与功率因数的影响关系和电流不平衡度的主要分布情况.结果表明,该牵引变电所负荷和负序电流概率模型,具有较强的适应性和适用性,能够比较真实地模拟高速铁路的运行特性和负序电流分布情况.

The mechanical characteristics of jointed rocks are mainly controlled by geometrical features of intermittent joints. The failure mechanism and shear behavior of discontinuous joints are simulated by series of direct shear tests using the particle flow code (PFC2D). The effects of joint persistence, separation and azimuth angle on the failure strength of jointed rock are studied in detail. The results shows that peak shear strength can be considered as a function of the geometrical parameters, which is in a reasonable accordance with other experimental results. Meanwhile, as for failure mechanism, joint separation and azimuth angle are especially discussed, and specific damage patterns are presented as well.

This paper develops general invariant representations of the constitutive equations for isotropic nonlinearly elastic materials. Different sets of mutually orthogonal unit tensor bases are constructed from the strain argument tensor by using the representation theorem and corresponding irreducible invariants are defined. Their relations and geometrical interpretations are established in three dimensional principal space. It is shown that the constitutive law linking the stress and strain tensors is revealed to be a simple relationship between two vectors in the principal space. Relative to two different sets of the basis tensors, the constitutive equations are transformed according to the transformation rule of vectors. When a potential function is assumed to exist, the vector associated with the stress tensor is expressed in terms of its gradient with respect to the vector associated with the strain tensor. The Hill’s stability condition is shown to be that the scalar product of the increment of those two vectors must be positive. When potential function exists, it becomes to be that the 3×3 constitutive matrix derived from its second order derivative with respect to the vector associated with the strain must be positive definite. By decomposing the second order symmetric tensor space into the direct sum of a coaxial tensor subspace and another one orthogonal to it, the closed form representations for the fourth order tangent operator and its inversion are derived in an extremely simple way.