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The capacity of distributed photovoltaic impacts the safe and reliable operation of the distribution network. Energy storage in distribution feeders has emerged as a pivotal solution for consuming distributed photovoltaic. However, it is challenged to balance the economic viability of energy storage investing and grid reliability. In response, a method of optimizing the configuration of energy storage in distribution feeders based on the new model of counter configuration of the renewables from energy storage is proposed, which takes into account both the economy and reliability aspects. Firstly, an economic model for optimizing energy storage in distribution feeders is established. Secondly, a grid reliability simulation is performed based on the results of the economic optimization of energy storage in distribution feeders. Finally, considering the comprehensive indexes of economy and reliability, the different nodes are evaluated for site selection using technique for order preference by similarity to ideal solution (TOPSIS), and the energy storage configuration scheme is determined by the cumulative distribution curve. The simulation results show that the consuming capacity of distributed photovoltaic in the distribution feeders is effectively improved, and better balance the economic viability of energy storage investing with grid reliability, which can be used as a reference for the reasonable determination of ratios of the energy storage for renewables.

The Cloud-based Liquid Pipeline Simulator(CloudLPS) is a universal liquid pipeline simulation software with completely independent intellectual property rights in China. Herein, its applications are introduced. Its operation processes from pipeline network modeling and simulation calculation to result viewing are elaborated based on a specific case. Then, calculations are performed with CloudLPS for various operating conditions, including steady-state transportation, transient water hammer, and alternating transportation of cold and hot oil, after which the results are compared with those obtained from the foreign commercial software SPS and TLNET, making the discovery that the CloudLPS has comparable accuracy in terms of hydrodynamic and thermal simulations. Subsequently, CloudLPS is applied to solve practical pipeline problems such as normal-temperature transportation, heated transportation, and alternating transportation of cold and hot oil. By comparing the results of these practical applications with the field data, it is proven that the CloudLPS can meet the requirements of the actual engineering calculations. Finally, the two special features of CloudLPS, including visual control logic construction and energy consumption optimization, are presented. The results of this paper indicate that CloudLPS is of great competence in hydrodynamic and thermal simulations, logic control, and energy consumption optimization.

A domestic cloud simulation software platform named CloudLPS(Cloud-Based Liquid Pipeline Simulator) with complete independent intellectual property rights is developed, aiming to eliminate the long-term reliance on foreign commercial simulation softwares for liquid pipeline simulation. The platform is built on a B/S cloud architecture and is designed with the goals of accuracy, speed, simple interfaces, and convenient interaction. In terms of models, in addition to conventional flow and heat transfer models, CloudLPS also introduces the advanced mathematical models such as the non-Newtonian fluid model and the cross-dimensional thermal model, which enables the software to have the capability of hydraulic and thermal simulations for condensable and high-viscosity crude oil pipelines. Furthermore, the software also introduces some efficient simulation methods,such as adaptive mesh, dynamic mesh, divide and conquer, and heterogeneous parallelization, to facilitate the rapid calculation of large and complex pipe network system. In terms of functionality, it primarily offers users a variety of business and composite business function support to crude oil and refined oil pipeline networks, such as steady-state and unsteady-state simulations of pipeline network system, logic control, water hammer analysis,batch tracking, shutdown and restart, energy consumption optimization, and more. Additionally, the software provides a user-friendly visual modeling and result data display and analysis environment. CloudLPS serves as a fundamental software tool that plays a supportive role in various aspects of pipeline network system, such as planning and designing, operation scheduling, risk decision-making, and other business scenarios. Its application holds significant importance in ensuring the safe and economic operation of liquid pipelines in China.

Gold content of a low-grade gold ore in Guangxi is 1.29 g/t, the gangue minerals are mainly quartz, and the content of non-ferrous metals such as copper, lead, zinc and other harmful elements arsenic is extremely low. The cyanide gold leaching test was carried out on the ore, and the effects of grinding fineness, pH value of solution, cyanide dosage, stirring speed and leaching time on the gold leaching effect were investigated respectively; The leaching aids with better leaching effect were mixed in the same proportion, and three new leaching aids A, B and C were obtained; for a low-grade gold ore in Guangxi, a cyanide leaching aid leaching test was carried out. The results show that the sample fineness of -0.074 mm accounts for 93.27%, the pH value of the solution is 10.5, the dosage of potassium cyanide is 4 kg/t, the stirring speed is 1500 r/min, and the leaching time is 24 h, the gold leaching rate is 24 h. 92.58%; the amount of potassium cyanide was reduced to 3 kg/t, and the other conditions remained unchanged, after adding the new leaching aid A for 18 h, the leaching rate of gold could reach more than 93%. The addition of the new leaching aid effectively improves the leaching rate of gold, reduces the loss of cyanide by 25%, and shortens the leaching time by more than 6 h.

The stability of the inspection robot during crossing the jumper lines of the live lines of the 110kV power transmission is very important. In this study, a novel inspection robot was designed, equipped with a centroid adjusting mechanism to increase the stability during crossing the jumper line. The various obstacle crossing modes of the robot and the corresponding planning are designed. The dynamic modeling method for the rigid-flexible coupling of the robot in the flexible cable environment is studied, which helps to understand the influence of the robot on the transmission line in the environment. The simulation is carried out to analyze the deformation of the jumper line when the robot is on it. The results can be utilized to plan the motion to improve the robot’s efficiency and stability when the robot crosses the jumper line.

Accurate obstacle detection and proper behavior planning are key factors in the success of transmission line inspection robots. To achieve the autonomous location and identification of line obstacles in the operation of transmission line inspection robots, we propose a method in which information from various sensors is used to control a robot such that it can reliably and stably approach, locate, and identify obstacles. The accuracy and real-time requirements of obstacle information in the autonomous operation of the inspection robot are analyzed, a multi-sensor integrated structure for line obstacle location and identification is proposed, and an obstacle location and identification algorithm is designed for each stage. The principle of monocular vision ranging is used to control the robot such that it approaches obstacles and enters the short-range location stage, where the reliable location of obstacles is achieved via collision-encoder-current sensor data fusion. Obstacles are identified and the identification results are combined with those of the approaching rolling phase. The robot state vector is constructed according to the obstacle detection information and information measured by the robot sensors. Based on the current state vector, combined with the robot obstacle surmounting process, an obstacle surmounting behavior planning method based on multi-sensor fusion is implemented. Experiments were conducted using laboratory-simulated lines to verify the effectiveness of the proposed method for obstacle detection and behavior planning during transmission line inspection.

The spread of infections and rot are crucial factors in the decrease in tomato production. Accurately segmenting the affected tomatoes in real-time can prevent the spread of illnesses. However, environmental factors and surface features can affect tomato segmentation accuracy. This study suggests an improved YOLOv8s-Seg network to perform real-time and effective segmentation of tomato fruit, surface color, and surface features. The feature fusion capability of the algorithm was improved by replacing the C2f module with the RepBlock module (stacked by RepConv), adding SimConv convolution (using the ReLU function instead of the SiLU function as the activation function) before two upsampling in the feature fusion network, and replacing the remaining conventional convolution with SimConv. The F1 score was 88.7%, which was 1.0%, 2.8%, 0.8%, and 1.1% higher than that of the YOLOv8s-Seg algorithm, YOLOv5s-Seg algorithm, YOLOv7-Seg algorithm, and Mask RCNN algorithm, respectively. Meanwhile, the segment mean average precision (segment mAP@0.5) was 92.2%, which was 2.4%, 3.2%, 1.8%, and 0.7% higher than that of the YOLOv8s-Seg algorithm, YOLOv5s-Seg algorithm, YOLOv7-Seg algorithm, and Mask RCNN algorithm. The algorithm can perform real-time instance segmentation of tomatoes with an inference time of 3.5 ms. This approach provides technical support for tomato health monitoring and intelligent harvesting.

To improve the environment perception ability of wheeled mobile robots (WMRs), the visual behavior mechanism of the negative-correlation motion of chameleons is introduced into the binocular vision system of WMRs, and a shifty-behavior-based environment perception model with chameleon-inspired active vision for WMRs is established, where vision–motor coordination is achieved. First, a target search sub-model with chameleon-inspired binocular negative-correlation motion is built. The relationship between the rotation angles of two cameras and the neck and the camera’s field of view (FOV), overlapping angle, region of interest, etc., is analyzed to highlight the binocular negative-correlation motion compared with binocular synchronous motion. The search efficiency of the negative-correlation motion is doubled compared with binocular synchronous motion, and the search range is also greatly improved. Second, the FOV model of chameleon-inspired vision perception based on a shifty-behavior mode is set up. According to the different functional requirements of target searching and tracking stages, the shift of the robot visual behavior is analyzed from two aspects, measuring range and accuracy. Finally, a chameleon-inspired active-vision-based environment perception strategy for mobile robots is constructed based on the shifty-behavior mode, and experimental verification is deployed, which achieves the reproduction of the visual behavior of chameleons in the vision system of mobile robots with satisfactory results.

This paper elaborates on a digital simulation study on supernormal particle flow used to investigate and analyze the process of picking up jujube branches, which was a meaningful attempt to search for accurate and effective advanced numerical analogy methods in the agricultural field. In this paper, the meshless technology based on the element-free Galerkin method was used for the first time to present the effects of particle size, particle number and particle acting force on the movement of irregular particles, and the influence of the gear rotation speed, the feeding amount, and the jujube branch size on the movement behavior as well as the picking rate. It can describe not only the particles’ dynamic movement in the process of picking up jujube twigs, such as feeding, collision, throwing and rolling, but also the effect of the quality and shape caused by the particle size, which in turn affects the surface force of particles and interparticle acting force, thereby affecting the weight function in the analytical solution, the total feeding amount and the effect of the acting force resulting from the particles’ contact, roll and collision caused by gear rotation. The findings reveal that the digital simulation, based on the meshless Galerkin technology and Rocky software, is effective in dealing with issues related to supernormal particle flow. By eliminating the influence of geometric shapes on calculations, the method boasts an effective solution to the movement problems of irregularly shaped particles, which would be further applied in the agriculture field.

Pancreatic cancer (PC) is an aggressive malignant disease. Pancreatic ductal adenocarcinoma (PDAC) is a main type of PDAC. The inhibition of aerobic glycolysis in PC cells is one of the approaches to treat PDAC. WD repeat protein 79 (WDR79) acts as a scaffold protein and is involved in several physiological processes. Since WDR79 affects the progression of several types of cancers, whereas its role in PDAC remains unclear. This study was aimed to investigate the role of WDR79 in the progression of PDAC and clarify the mechanism. We found that WDR79 was highly expressed in PDAC cells. Knockdown of WDR79 inhibited the growth as well as the motility of PDAC cells, while overexpression of WDR79 contributed to the growth and motility. The ablation of WDR79 restrained aerobic glycolysis of PDAC cells. Mechanically, we found that WDR79 depletion increased SIRT4 expression by suppressing UHRF1 expression, which counteracted the function of WDR79 in PDAC. We thought that WDR79 could serve as a target for treating PDAC.

In order to optimize the heat preservation capacity of Chinese solar greenhouse (CSG) and further reduce energy consumption, we clarified the mechanism of the external thermal insulation layer that affects the microclimate environment of CSG. The most suitable external insulation layer thickness (EILT) of the solar greenhouse envelope structure in high latitude and cold region has been indicated. A three-dimensional mathematical model was developed based on computational fluid dynamics and verified using experimental measurement. The temperature variations, heat variations and economic benefit were analysed. The results indicated that covering the outer surface of the enclosures with a thermal insulation layer could effectively increase the greenhouse temperature by 1.2–4.0°C. The influence degree of the external thermal insulation layer on the greenhouse microclimate was as follows: sidewall (SW) > north wall (NW) > north roof (NR). In high-dimensional and cold areas, covering the outer surface of all enclosures with insulation layer as the suitable solution could raise the greenhouse air temperature maximally. The suitable EILT of each maintenance structure was obtained as follows: NW 80 mm, SW 80 mm, NR 100 mm.

The distribution law of residual stress in multi-channel scanned plasma cladding of Co-based alloy under different process parameters was studied by means of simulation and tests, and the optimum process parameters were optimized. The simulation model of the plasma cladding stress field was established by ABAQUS software, and the influence law of the working current, scanning speed, and scanning mode on the residual stress of the Co-based alloy multi-channel scanning was studied. A set of optimal cladding process parameters were obtained. The residual stress of the cladding layer was measured by the blind hole method and compared with the stress value in the finite element model. The results show that there is residual tensile stress on the surface of the cladding layer. The residual stress along the direction of the scanning path is greater than that along the direction of the scan sequence. The residual stress increases with the increase of the working current. The scanning speed is greater, and the residual stress is smaller. The residual stress of the short-edge scanning is greater than that of the long-edge scanning. The residual stress of the successive scanning is greater than that of the reciprocating scanning. The long-edge reciprocating scanning is the best scanning mode. The best combination of process parameters is the working current of 90 A, the scanning speed of 100 mm/min, and the long-edge reciprocating scanning mode.

This paper clarified the mechanism of the south and north roofs' effect on the thermal environment of the Chinese solar greenhouse (CSG), using a new parameter: ridge position ratio (RPR), which can describe the dynamic dependency relationship between the south and north roofs. A mathematical model was established using a method of combining computational fluid dynamics (CFD) simulation with experiments, then the model was used to further analyse the effect of RPR on the thermal environment of the CSG. The experimental greenhouse was simulated as an empty building to obtain results independently from these factors including crop and ventilation conditions. The results showed that the occurrence time of the maximum air temperature will be delayed when RPR increases to 0.3 during the daytime. As RPR increases, the heat storage layer of the soil gradually becomes thinner, but the north wall remains unchanged. RPR has a relatively small effect on the minimum temperature of each greenhouse part during the night. Mathematical models of the relationships between RPR, the solar energy that entered the greenhouse and the released heat energy of the enclosure structures were established, respectively. This paper can provide theoretical guidance for the structural design of the CSG.

Abstract Background In the field of drug repositioning, it is assumed that similar drugs may treat similar diseases, therefore many existing computational methods need to compute the similarities of drugs and diseases. However, the calculation of similarity depends on the adopted measure and the available features, which may lead that the similarity scores vary dramatically from one to another, and it will not work when facing the incomplete data. Besides, supervised learning based methods usually need both positive and negative samples to train the prediction models, whereas in drug-disease pairs data there are only some verified interactions (positive samples) and a lot of unlabeled pairs. To train the models, many methods simply treat the unlabeled samples as negative ones, which may introduce artificial noises. Herein, we propose a method to predict drug-disease associations without the need of similarity information, and select more likely negative samples. Results In the proposed EMP-SVD (Ensemble Meta Paths and Singular Value Decomposition), we introduce five meta paths corresponding to different kinds of interaction data, and for each meta path we generate a commuting matrix. Every matrix is factorized into two low rank matrices by SVD which are used for the latent features of drugs and diseases respectively. The features are combined to represent drug-disease pairs. We build a base classifier via Random Forest for each meta path and five base classifiers are combined as the final ensemble classifier. In order to train out a more reliable prediction model, we select more likely negative ones from unlabeled samples under the assumption that non-associated drug and disease pair have no common interacted proteins. The experiments have shown that the proposed EMP-SVD method outperforms several state-of-the-art approaches. Case studies by literature investigation have found that the proposed EMP-SVD can mine out many drug-disease associations, which implies the practicality of EMP-SVD. Conclusions The proposed EMP-SVD can integrate the interaction data among drugs, proteins and diseases, and predict the drug-disease associations without the need of similarity information. At the same time, the strategy of selecting more reliable negative samples will benefit the prediction.

Abstract Background Bacterial small non-coding RNAs (sRNAs) have emerged as important elements in diverse physiological processes, including growth, development, cell proliferation, differentiation, metabolic reactions and carbon metabolism, and attract great attention. Accurate prediction of sRNAs is important and challenging, and helps to explore functions and mechanism of sRNAs. Results In this paper, we utilize a variety of sRNA sequence-derived features to develop ensemble learning methods for the sRNA prediction. First, we compile a balanced dataset and four imbalanced datasets. Then, we investigate various sRNA sequence-derived features, such as spectrum profile, mismatch profile, reverse compliment k-mer and pseudo nucleotide composition. Finally, we consider two ensemble learning strategies to integrate all features for building ensemble learning models for the sRNA prediction. One is the weighted average ensemble method (WAEM), which uses the linear weighted sum of outputs from the individual feature-based predictors to predict sRNAs. The other is the neural network ensemble method (NNEM), which trains a deep neural network by combining diverse features. In the computational experiments, we evaluate our methods on these five datasets by using 5-fold cross validation. WAEM and NNEM can produce better results than existing state-of-the-art sRNA prediction methods. Conclusions WAEM and NNEM have great potential for the sRNA prediction, and are helpful for understanding the biological mechanism of bacteria.

Chinese solar greenhouses are unique facility agriculture buildings and widely used in northeastern China, providing a favorable requirement for crop growth. The north wall configurations play an essential role in heat storage and thermal insulation and directly affect the management of the internal environment. This research is devoted to further improve the thermal performance of the greenhouse and explore the potential of the north wall. A mathematical model was designed to investigate the concave-convex wall configurations based on computational fluid dynamics. Four passive heat-storage north walls were analyzed by using the same constituent materials, including a plane wall, a vertical wall, a horizontal wall and an alveolate wall. The numerical model was validated by experimental measurements. The temperature distributions of the north walls were examined and a comparative analysis of the heat storage-release capabilities was carried out. The results showed that the heat-storage capacity of the north wall is affected by the surface structure. Moreover, the critical factor influencing the air temperature is the sum of the heat load released by the wall and the energy increment of greenhouse air. The results suggested that the alveolate wall has preferable thermal accumulation capacity. The concave-convex wall configurations have a wider range of heat transfer performance along the thickness direction, while the plane wall has a superior thermal environment. This study provides a basic theoretical reference to rationally design the internal surface structures of the north wall.

Natural ventilation is an effective energy-saving strategy conducive to promoting sustainable agricultural production. A comprehensive numerical simulation is performed to predict the airflow pattern and thermal behavior in different arched greenhouses. The defined arc chord angle and position angle are employed to examine the natural ventilation process and corresponding roof vent scenarios. The numerical simulation is compared with the experimental data and good agreements are observed. Various configurations of ventilated structures, wind conditions and ventilation layouts are simulated on a high-resolution polyhedral grid based on a grid sensitivity analysis, which is beneficial to the optimization of greenhouse cooling combined with the water circulation heat collection system. The cooling effect in summer is analyzed by estimating the ventilation flow rate and microclimate inhomogeneity. The results demonstrate that the position angle of 85° of the arched greenhouses is an optimum ventilation direction and its impact on the microclimate is marginally affected by the change of the ventilation structure. The designed ventilation scheme has perfect air exchange capacity and cooling effect because the average air temperature can be reduced by 1.5°C more than the existing greenhouse in 10 minutes of ventilation. Likewise, the results show that the temperature and velocity inhomogeneities are approximately decreased by 33.3% and 11.89%, respectively. The practical value of the research is expected to provide basic quantitative conclusions for evaluating the natural ventilation performance.

Abstract Background Drug-disease associations provide important information for the drug discovery. Wet experiments that identify drug-disease associations are time-consuming and expensive. However, many drug-disease associations are still unobserved or unknown. The development of computational methods for predicting unobserved drug-disease associations is an important and urgent task. Results In this paper, we proposed a similarity constrained matrix factorization method for the drug-disease association prediction (SCMFDD), which makes use of known drug-disease associations, drug features and disease semantic information. SCMFDD projects the drug-disease association relationship into two low-rank spaces, which uncover latent features for drugs and diseases, and then introduces drug feature-based similarities and disease semantic similarity as constraints for drugs and diseases in low-rank spaces. Different from the classic matrix factorization technique, SCMFDD takes the biological context of the problem into account. In computational experiments, the proposed method can produce high-accuracy performances on benchmark datasets, and outperform existing state-of-the-art prediction methods when evaluated by five-fold cross validation and independent testing. Conclusion We developed a user-friendly web server by using known associations collected from the CTD database, available at http://www.bioinfotech.cn/SCMFDD/. The case studies show that the server can find out novel associations, which are not included in the CTD database.

It is generally believed that bioavailability (F) calculated based on systemic concentration area under the curve (AUC) measurements cannot exceed 1.0, yet some published studies report this inconsistency. We teach and believe, based on differential equation derivations, that rate of absorption has no influence on measured systemic clearance following an oral dose, i.e., determined as available dose divided by AUC. Previously, it was thought that any difference in calculating F from urine data versus that from systemic concentration AUC data was due to the inability to accurately measure urine data. A PubMed literature search for drugs exhibiting F > 1.0 and studies for which F was measured using both AUC and urinary excretion dose-corrected analyses yielded data for 35 drugs. We show and explain, using Kirchhoff's Laws, that these universally held concepts concerning bioavailability may not be valid in all situations. Bioavailability, determined using systemic concentration measurements, for many drugs may be overestimated since AUC reflects not only systemic elimination but also absorption rate characteristics, which is most easily seen for renal clearance measures. Clearance of drug from the absorption site must be significantly greater than clearance following an iv bolus dose for F(AUC) to correctly correspond with F(urine). The primary purpose of this paper is to demonstrate that studies resulting in F > 1.0 and/or greater systemic vs urine bioavailability predictions may be accurate. Importantly, these explications have no significant impact on current regulatory guidance for bioequivalence testing, nor on the use of exposure (AUC) measures in making drug dosing decisions.

LncRNA-protein interactions play important roles in post-transcriptional gene regulation, poly-adenylation, splicing and translation. Identification of lncRNA-protein interactions helps to understand lncRNA-related activities. Existing computational methods utilize multiple lncRNA features or multiple protein features to predict lncRNA-protein interactions, but features are not available for all lncRNAs or proteins; most of existing methods are not capable of predicting interacting proteins (or lncRNAs) for new lncRNAs (or proteins), which don't have known interactions. In this paper, we propose the sequence-based feature projection ensemble learning method, "SFPEL-LPI", to predict lncRNA-protein interactions. First, SFPEL-LPI extracts lncRNA sequence-based features and protein sequence-based features. Second, SFPEL-LPI calculates multiple lncRNA-lncRNA similarities and protein-protein similarities by using lncRNA sequences, protein sequences and known lncRNA-protein interactions. Then, SFPEL-LPI combines multiple similarities and multiple features with a feature projection ensemble learning frame. In computational experiments, SFPEL-LPI accurately predicts lncRNA-protein associations and outperforms other state-of-the-art methods. More importantly, SFPEL-LPI can be applied to new lncRNAs (or proteins). The case studies demonstrate that our method can find out novel lncRNA-protein interactions, which are confirmed by literature. Finally, we construct a user-friendly web server, available at http://www.bioinfotech.cn/SFPEL-LPI/.

The numerical simulation efficiency of large-scale natural gas pipeline network is usually unsatisfactory. In this paper, Graphics Processing Unit (GPU)-accelerated hydraulic simulations for large-scale natural gas pipeline networks are presented. First, based on the Decoupled Implicit Method for Efficient Network Simulation (DIMENS) method, presented in our previous study, a novel two-level parallel simulation process and the corresponding parallel numerical method for hydraulic simulations of natural gas pipeline networks are proposed. Then, the implementation of the two-level parallel simulation in GPU is introduced in detail. Finally, some numerical experiments are provided to test the performance of the proposed method. The results show that the proposed method has notable speedup. For five large-scale pipe networks, compared with the well-known commercial simulation software SPS, the speedup ratio of the proposed method is up to 57.57 with comparable calculation accuracy. It is more inspiring that the proposed method has strong adaptability to the large pipeline networks, the larger the pipeline network is, the larger speedup ratio of the proposed method is. The speedup ratio of the GPU method approximately linearly depends on the total discrete points of the network.

A novel mechanism for 110 kV power transmission line inspection is presented according to the requirements of inspection tasks and characteristics of obstacles on power transmission lines. Its configuration is introduced, and the navigation process of typical obstacles is planned. Then the kinematics equations and the workspace are derived. The grade ability of the power transmission line inspection robot is analysed and then improved by some measures. The quasi-static mode of the robot and the contact mechanics of the wheels are established. Adhesion force of the driving wheel can be improved by optimizing the structure of the wheel and the wheel load is reasonably distributed by planning the posture of the robot. So the climbing ability is ameliorated. The climbing ability of the different wheel structure and the different posture is simulated. Furthermore, the simulation of the navigation of the strain clamp and test of navigation of the jumper in the laboratory have been carried out. The simulation results demonstrate that the mechanism has such characteristics as compact construction, strong stability and excellent obstacle negotiation capability.

The implicit finite difference method is one of the most widely applied methods for transient natural gas simulation. However, this implicit method is associated with high computational cost. To improve the simulation efficiency of implicit finite difference method, an adaptive strategy is introduced into the simulation process. The proposed adaptive strategy consists of the adaptive time step strategy and the adaptive spatial grid strategy. And these two parts are implemented based on the local error technique and the multilevel grid technique respectively. The results illustrate that the proposed adaptive method can automatically and independently adjust the time step and the spatial grid system according to the gas flow state in the simulation process, and demonstrates a significant advantage in terms of computational accuracy and efficiency compared with the non-adaptive method.