Study of the pyrolysis mechanism of SiBCN polymer precursor

The pyrolysis mechanisms occurring during the conversion of polyborosilazane (PBSZ) into amorphous SiBCN cerasmic have been investigated. TGA–TDG experiment have been applied to investigate the mass loss behaviour during ceramization. Solid-state 11B, 13C and 29Si NMR spectroscopy has been applied to probe the local environment of all NMR active nuclei in the precursor, the thermolysis intermediates and the ceramic residue. IR spectroscopy has been performed to receive valuable information on the chemical bonding in all materials. At temperature below 400oC, Si-N bonds are formed via condensation reaction involving N-H and Si-H units with hydrogen released. It is followed by evolution of hydrocarbons due to the cleavage of bonds and formation of methane and hydrogen at 600 oC. After heating to 1000 oC, ceramization complete and free carbon, BN3 domains as well as Si–C–N units coexist SiCxN4-x,x=0,1,2,3. And BN3 keep unchanged during the whole ceramization stage

A Prediction Model for Ultra-Short-Term Output Power of Wind Farms Based on Deep Learning

The output power prediction of wind farm is the key to effective utilization of wind energy and reduction of wind curtailment. However, the prediction of output power has long been a difficulty faced by both academia and the wind power industry, due to the high stochasticity of wind energy. This paper attempts to improve the ultra-short-term prediction accuracy of output power in wind farm. For this purpose, an output power prediction model was constructed for wind farm based on the time sliding window (TSW) and long short-term memory (LSTM) network. Firstly, the wind power data from multiple sources were fused, and cleaned through operations like dimension reduction and standardization. Then, the cyclic features of the actual output powers were extracted, and used to construct the input dataset by the TSW algorithm. On this basis, the TSW-LSTM prediction model was established to predict the output power of wind farm in ultra-short-term. Next, two regression evaluation metrics were designed to evaluate the prediction accuracy. Finally, the proposed TSW-LSTM model was compared with four other models through experiments on the dataset from an actual wind farm. Our model achieved a super-high prediction accuracy 92.7% as measured by d_MAE, an evidence of its effectiveness. To sum up, this research simplifies the complex prediction features, unifies the evaluation metrics, and provides an accurate prediction model for output power of wind farm with strong generalization ability

Characteristics of gas-oil contact and mobilization limit during gas-assisted gravity drainage process

Gravity can reduce the instability of the gas-oil contact that is caused by gas channeling in locations with low flow resistance, such as high-permeability layers, macropores, and fractures during the gas-assisted gravity drainage process. Herein, the microscopic forces during the gas-assisted gravity drainage process were analyzed and combined with the capillary model to study the occurrence boundary of gas-assisted gravity drainage process, and the characteristics of the gas-oil contact in the gas-assisted gravity drainage process was discussed. The results show that free gravity drainage occurs only in pores where a certain height of the oil column and pore radius are reached. Furthermore, the lower the oil-gas interface migration rate, the easier free gravity drainage occurs. In other scenarios, additional gas injection is required. During the gas-assisted gravity drainage process, the gas-oil contact moves down stably as a transition. The width of the transition zone and the available pore radius are related to the gas-oil contact migration rate and the oil viscosity; the smaller the gas-oil contact migration rate and the lower the oil viscosity, the smaller pore throat can be involved in mobilization. Optimizing the gas injection rate and reducing the oil viscosity can delay the gas channeling maturity time, which is beneficial for the realization of the gas-assisted gravity drainage process. Finally, a method considering micropore heterogeneity is established for determining the critical gas injection rate, while the mainstream pore throat can be involved in mobilization and the gas-oil contact can be stabilized at the same time. The method of determining the critical gas injection rate can help researchers and reservoir engineers to better understand and implement the gas-assisted gravity drainage process.Cited as: Kong, D., Gao, J., Lian, P., Zheng, R, Zhu, W., Xu, Y. Characteristics of gas-oil contact and mobilization limit during gas-assisted gravity drainage process. Advances in Geo-Energy Research, 2022, 6(2): 169-176. https://doi.org/10.46690/ager.2022.02.0

Domain Adaptive Code Completion via Language Models and Decoupled Domain Databases

Large Language Models (LLMs) have demonstrated remarkable performance in code completion. However, due to the lack of domain-specific knowledge, they may not be optimal in completing code that requires intensive domain knowledge for example completing the library names. Although there are several works that have confirmed the effectiveness of fine-tuning techniques to adapt language models for code completion in specific domains. They are limited by the need for constant fine-tuning of the model when the project is in constant iteration. To address this limitation, in this paper, we propose $k$NM-LM, a retrieval-augmented language model (R-LM), that integrates domain knowledge into language models without fine-tuning. Different from previous techniques, our approach is able to automatically adapt to different language models and domains. Specifically, it utilizes the in-domain code to build the retrieval-based database decoupled from LM, and then combines it with LM through Bayesian inference to complete the code. The extensive experiments on the completion of intra-project and intra-scenario have confirmed that $k$NM-LM brings about appreciable enhancements when compared to CodeGPT and UnixCoder. A deep analysis of our tool including the responding speed, storage usage, specific type code completion, and API invocation completion has confirmed that $k$NM-LM provides satisfactory performance, which renders it highly appropriate for domain adaptive code completion. Furthermore, our approach operates without the requirement for direct access to the language model's parameters. As a result, it can seamlessly integrate with black-box code completion models, making it easy to integrate our approach as a plugin to further enhance the performance of these models.Comment: Accepted by ASE202