China's deep coalbed methane resources have enormous potential. As a clean and high-quality energy, it is of great significance to ensure China's energy security and green and low-carbon development. The development of middle-shallow coalbed methane in China has basically been industrialized, but the development scale is far from the national planning goal. With the new progress made in the exploration and development of deep coalbed methane in recent years, it has provided new impetus for the rapid development of the coalbed methane industry. But when it comes to deep exploration and development, it faces a series of new geological, engineering, and other challenges that need to be tackled. In view of this, based on the latest national oil and gas resource evaluation data, the current situation of coalbed methane exploration and development in China was summarized, and the latest progress in deep coalbed methane exploration and development in typical blocks in China was evaluated. The new breakthroughs in geological evaluation, drilling, fracturing, and extraction of deep coalbed methane in recent years were summarized, and it is pointed out that there are problems in the current exploration and development of deep coalbed methane in China, such as low resource evaluation level, low adaptability of key technologies, and few mature demonstration projects that can be promoted and referenced.On this basis, four countermeasures and suggestions for the development of deep coalbed methane industry were proposed: strengthening resource exploration, strengthening key technical research, speeding up the construction of high-efficiency development demonstration zones, strengthening the co-exploration and co-exploitation of deep coal measures gas, so as to provide reference for promoting the exploration and development of deep coalbed methane in China.

In response to problems of rapid excavation of deep shafts, such as lining cracking and high support costs, based on the engineering background of -906~-1 158 m section of an overseas copper and gold mine, the support parameters of shaft were studied by theoretical calculations, numerical simulations and field tests. In order to restrict deformation of the shaft and reduce the cost of support reasonably and effectively, a shaft model was established based on the engineering practice, the stability of surrounding rock with the different parameters was analyzed by FLAC^3D numerical software combined with fluid-structure interaction. The results demonstrate that for the class Ⅲ surrounding rock, “anchor net spraying+steel fiber concrete” support is adopted, and its parameters are as follows: bolt diameter 22 mm, length 2.3 m, shotcrete thickness 50 mm, row spacing 1 m×1 m, steel fiber concrete thickness 550 mm. For the locally existing class Ⅳ~Ⅴ surrounding rock, “anchor net spray+foam board+steel fiber concrete” support was proposed, and the thickness of buffer layer foam board is 100 mm, and the thickness of steel fiber concrete is 600 mm. Field test results show that the average convergence rate of surrounding rock is 0.18~0.31 mm/d after 412 h excavation, which meets the requirements of air inlet shaft construction, and the construction efficiency is improved by about 23.5% compared with the domestic deep shaft. This work can provide a guidance for the support design of shaft in soft-fractured strata with water-rich.

A novel clustering approach combining Kmeans++ and PAM was introduced to segment the daily load curve chronologically for the dynamic reconfiguration of distribution networks incorporating time-varying wind solar power and loads. Multi-objective dynamic reconfiguration model of distribution network based on the optimal objectives of comprehensive cost, voltage offset and load balance. To enhance the computational efficiency of the model, an INOA(improved Nutcracker optimization algorithm) was proposed, which used Tent mapping+quasi-reflection learning to provide high-quality initial population. Dynamic fitness-distance balance selection method and tangential flight strategy were introduced to enhance the global search capability. The Cauchy-Gaussian variation perturbation was incorporated to augment the algorithm’s capability to escape from local optima. Using the IEEE 33-node system as a basis, the outcomes indicate that the suggested approach effectively achieves optimal load distribution and efficiently addresses the restructured model.

Small sensors powered by wind-solar hybrid power supply do not require regular replacement of the power supply, and can be deployed in various remote areas. However, in practice, the photovoltaic panels are prone to accumulation of dirt, resulting in reduced power generation, and the rotating parts of small wind turbines are also very prone to failure. A wind-solar hybrid power device with a simple structure and self-cleaning capability was designed, and its solar and wind energy collection performance was experimentally evaluated based on meteorological statistical data. Moreover, the vibration cleaning efficacy of the photovoltaic panels under various dust coverage levels was compared. The research results show that the device can generate a maximum output power of 77.28 mW under simulated clear weather conditions (light intensity of 948.1 W/m²). The starting wind speed of wind energy collection based on piezoelectric and vortex-induced vibration effects is around 1.5 m/s, and a wind speed of 4.3 m/s in the experiments can generate an output power of 4.63 mW. After vortex-induced vibration cleaning of the photovoltaic module with different dust coverage densities, the output power of solar energy collection can be restored to over 84% of the clean state. This study presents a new technical solution for maintenance-free small-scale micro power generation equipment in remote areas.

To solve the engineering problem of unclear standards and strong subjective experience when shield tunneling drivers set excavation parameters, which makes it difficult to control the shield tunneling attitude, an intelligent prediction model for shield tunneling attitude that considers the comprehensive effect of geological conditions, tunnel structure, and excavation parameters was proposed. Firstly, AWPSO (adaptive inertia weight particle swarm optimization) algorithm was established. Then, a shield attitude prediction model was constructed by combining GRU (gated recurrent unit) neural network, where the AWPSO algorithm was used to determine the optimal combination of hyperparameters in the GRU neural network. Finally, a case study was conducted to verify the on-site monitoring data between Zhangjiang Station and Resort Station on the Shanghai Suburban Railway Airport Connection Line. The results indicate that the proposed shield tunneling attitude prediction model based on AWPSO-GRU has high reliability and engineering practicality, which can provide reference and basis for setting construction parameters during shield tunneling.

Filling phase change capsules in a container to form a packed bed heat storage unit is a typical applica-tion of phase change capsules. Phase change capsules are usually stacked in a specific layout in the packed bed flow channel. Studying the heat storage and release characteristics of a single phase change capsule in a packed bed flow channel can help optimize the design of a medium-temperature phase change heat storage system. Therefore, a two-dimensional packed bed numerical model of phase change cap-sules was established. The heat transfer and flow characteristics of the external heat transfer fluid flowing through the phase change capsules in the direction of gravity, counter gravity and vertical gravity were compared and studied. The effects of flow rate, temperature and capsule diameter on the melting process of phase change capsules were studied. The results show that the heat transfer rate of the windward side of the phase change capsule in the packed bed channel is faster. Due to the thermal resistance of the cavity air and the natural convection, the complete solidification time is the shortest when the heat transfer fluid flows countercurrently. Compared with the downstream flow, the complete solidification time of the up-stream flow is shortened by 8.9%. When the diameter of the phase change capsule is 12 mm, the melting speed of the phase change capsule with PTFE as the wall material in the center is 1.45% slower than that of the 304 stainless steel phase change capsule, and the average heat storage rate is 1.5% lower. The melting rate of the phase change capsule with modified PTFE as the wall material cavity in the center is 6.9% faster than that of the 304 stainless steel phase change capsule, and the average heat storage rate is 5.8% higher. Increasing the HTF inlet velocity and temperature can increase the average heat storage rate of the phase change capsule and shorten the melting time of the phase change capsule. The heat storage and release characteristics of the capsule have important guiding significance for the design optimization and practical application of the capsule monomer and the medium temperature phase change heat storage system.

The data-driven approach of machine learning enables the intelligent construction of TBM(tunnel boring machines), which is crucial for optimizing the tunneling process, improving the safety of tunneling and reducing labor costs. In order to solve the problems of excessive noise, redundant parameters and difficult effective feature extraction in TBM operation data, a data-driven machine learning method was used to mine the complex machine-soil interaction contained in the data and realize the classification and prediction of TBM surrounding rock mass. First, for the large amount of operational data generated during TBM tunneling, the KDE (kernel density estimation) method was used to extract features from typical tunneling parameter curves, and the maximum probability of the key operating parameters during stable tunneling stage of TBM is obtained. Then, based on the actual TBM operation data, an integrated learning algorithm for surrounding rock classification stacking was proposed. The algorithm is further optimized through k-fold cross-validation, and the complex relationships in the data are mined by using the two-layer learning framework of base classifier and meta-classifier. Finally, a data set of 5 868 TBM segments was used to verify the effectiveness of the proposed algorithm. The results show that the average F₁ of the four-classification problem is 0.705, and the average F₁ of the two-classification problem is 0.797, which are better than the four selected base classifiers.

In the southern Great Xing'an Range, the Xishala area exposes a substantial amount of Mesozoic strata of Xinmin Formation (J₂x) and Manketouebo Formation (J₃m). However, due to the presence of rhyolite in the rock association, it is easy to confuse the stratigraphic division and lead to disputes of the stratigraphic attribution of rhyolite. Therefore, zircon U-Pb dating and geochemical characteristic analysis of the rhyolites were conducted, and stratigraphic sections of the Xinmin and Manketouebo Formations were compared to explore the formation age, tectonic setting, and stratigraphic attribution of the rhyolites. The results indicate that the geochemical characteristics of the riolites in Xishala area are high SiO₂ content (The value of mean is 75.74%.), rich alkali content [mean(Na₂O+K₂O)=7.69%] and low Mg and Ca content, belonging to peraluminous and high-K calc-alkaline series. The Chondrite-normalized REE pattern is right-dip type, with relative enrichment of LREE and relative depletion of HREE, the value of (La/Yb)_Nis between 10.61~14.21 and strong negative Eu anomaly (The value of δEu is between 0.44~0.53.). LILE(large ion lithophilic elements) such as Rb, Ba and K are relatively enriched, while HFSE(high field strength elements) such as Nb, Ta and Ti are relatively depleted, indicating that the magma originated from crustal materials. The LA-ICP-MS zircon U-Pb dating results show that the age of the rhyolite is (167.7±2.6) Ma, belonging to the Middle Jurassic, and it formes in the extensional tectonic action after the closure of the Mongolian-Okhotsk ocean. Based on the comparative study of regional geology and sectional rock assemblage types, the rhyolite belongs to the Manketouebo Formation.

The efficient utilization of electromagnetic spectrum resources has become a significant concern in the domain of wireless communications, with EMSM(electromagnetic spectrum map) playing a crucial role in visually representing spectrum usage within a specific task area and providing valuable support for the optimization of wireless networks. To address the challenges associated with generating fine-grained EMSMs under conditions of complex scenes and limited spatial point monitoring data, an improved DRN(deep residual network) model, ES-AFB(enhanced with a spatial attention feature block), was proposed. This model drew inspiration from image super-resolution techniques and leveraged the strong spatial characteristics of EMSMs to design a deep residual network capable of extracting the correlation and spectral features of EMSMs. The enhanced spatial attention feature block was utilized to mine the intrinsic implicit spatial features of coarse-grained EMSMs. Subsequently, the data size was reconfigured through the network’s multilayer up-sampling module, enabling the achievement of a more effective fine-grained image restoration. This approach allows for the generation of high-quality fine-grained EMSMs using limited coarse-grained monitoring data. The effectiveness of the algorithm is validated through simulation experiments, with the root-mean-square error of the EMSMs generated from actual data being found to be no more than 3%.

Formation RF (radio frequency) systems compatibility is an important comprehensive ability that affects the survival and combat effectiveness of aircraft formation. It needs to be effectively assessed and verified through flight tests, and simulation can further optimize test design and improve test efficiency. Thus, a flight test method for formation RF systems compatibility based on simulation prediction was proposed. Firstly, the development trend of RF compatibility and is its capability requirements were summarized and analyzed. Secondly, a simulation model of formation RF systems compatibility based on interference conflict was proposed, and a flight test method of formation RF systems compatibility based on the interference conflict distance from simulation analysis was established. Finally, simulation analysis and flight profile design were conducted for a typical formation RF compatibility test. This study has provided an effective method for the test profile design and flight verification of formation RF systems compatibility.