Optimization of Center of Gravity Position and Anti-Wave Plate Angle of Amphibious Unmanned Vehicle Based on Orthogonal Experimental Method

When the amphibious vehicle navigates in water,the angle of the anti-wave plate and the position of the center of gravity greatly influence the navigation characteristics.In the relevant research on reducing the navigation resistance of amphibious vehicles by adjusting the angle of the anti-wave plate,there is a lack of scientific selection of parameters and reasonable research of simulation results by using mathematical methods,and the influence of the center of gravity position on navigation characteristics is not considered at the same time.To study the influence of the combinations of the angle of the anti-wave plate and the position of the center of gravity on the resistance reduction characteristics,a numerical calculation model of the amphibious unmanned vehicle was established by using the theory of computational fluid dynamics,and the experimental data verified the correctness of the numerical model.Based on this numerical model,the navigation characteristics of the amphibious unmanned vehicle were studied when the center of gravity was located at different positions,and the orthogonal experimental design method was used to optimize the parameters of the angle of the anti-wave plate and the position of the center of gravity.The results show that through the parameter optimization analysis based on the orthogonal experimental method,the combination of the optimal angle of the anti-wave plate and the position of the center of gravity is obtained.And the numerical simulation result of resistance is consistent with the predicted optimal solution.Compared with the maximum navigational resistance,the parameter optimization reduces the navigational resistance of the amphibious unmanned vehicle by 24%.

Improvement of hydrogen permeation barrier performance by iron sulphide surface films

Fe–S compounds with hexagonal crystal structure are potential hydrogen permeation barrier during H2S corrosion. Hexagonal system Fe–S films were prepared on carbon steel through corrosion and CVD deposition, and the barrier effect of different Fe–S films on hydrogen permeation was tested using electrochemical hydrogen permeation method. After that, the electrical properties of Fe–S compound during phase transformation were measured using thermoelectric measurement system. Results show that the mackinawite has no obvious barrier effect on hydrogen penetration, as a p-type semiconductor, and pyrrhotite (including troilite) has obvious barrier effect on hydrogen penetration,as an n-type semiconductor. Hydrogen permeation tests showed peak permeation performance when the surface was deposited with a continuous film of pyrrhotite (Fe_(1–x)S) and troilite. The FeS compounds suppressed hydrogen permeation by the promotion of the hydrogen evolution reaction, semiconducting inversion from p-to n-type, and the migration of ions at the interface.

Pipeline Defect Detection Cloud System Using Role Encryption and Hybrid Information

Pipeline defect detection systems collect the videos from cameras of pipeline robots,however the systems always analyzed these videos by offline systems or humans to detect the defects of potential security threats.The existing systems tend to reach the limit in terms of data access anywhere,access security and video processing on cloud.There is in need of studying on a pipeline defect detection cloud system for automatic pipeline inspection.In this paper,we deploy the framework of a cloud based pipeline defect detection system,including the user management module,pipeline robot control module,system service module,and defect detection module.In the system,we use a role encryption scheme for video collection,data uploading,and access security,and propose a hybrid information method for defect detection.The experimental results show that our approach is a scalable and efficient defection detection cloud system.

Core-shell-embedded Mesoporous Silica Capsules for Atmospheric Water Harvesting

A one-step ultrasonic mechanical method was used to synthesize a kind of atmospheric water harvesting material with high water harvesting performance in a wide relative humidity(RH)range,especially at low RH(RH<40%),namely,mesoporous silica capsule(MSC)with core-shell structure.Transmission electron microscopy(TEM),nitrogen adsorption and other characterization techniques were used to study the formation process of nano-microspheres.A new mechanism of self-adaptive concentration gradient regulation of silicon migration and recombination core-shell structure was proposed to explain the formation of a cavity in the MSC system.The core-shell design can enhance the specific surface area and pore volume while maintaining the monodispersity and mesoporous size.To study the water harvesting performance of MSC,solid silica nanoparticles(SSN)and mesoporous silica nanoparticles(MSN)were prepared.In a small atmospheric water collection test(25℃,40%RH),the water vapour adsorption and desorption kinetics of MSC,SSN,MSN and a commercial silica gel(CSG)were compared and analyzed.The results show that the MSC with mesoporous channels and core-shell structure can provide about 0.324 gwater/gadsorbent,79%higher than the CSG(0.181 gwater/gadsorbent).It is 25.1%higher than that of 0.259 gwater/gadsorbentof un-hollowed MSN and 980%higher than that of0.03 gwater/gadsorbentof un-hollowed SSN.The material has a large specific surface area and pore volume,simple preparation method and low cost,which provides a feasible idea for realising atmospheric water collection in arid and semi-arid regions.

Thermodynamic Performance Analysis of E/F/H-Class Gas Turbine Combined Cycle with Exhaust Gas Recirculation and Inlet/Variable Guide Vane Adjustment under Part-Load Conditions

Exhaust gas recirculation control(EGRC),an inlet air heating technology,can be utilized in combination with inlet/variable guide vane control(IGV/VGVC) and fuel flow control(FFC) to regulate the load,thereby effectively improving the part-load(i.e.,off-design) performance of the gas turbine combined cycle(GTCC).In this study,the E-,F-,and H-Class EGR-GTCC design and off-design system models were established and validated to perform a comparative analysis of the part-load performance under the EGR-IGV-FFC and conventional IGV-FFC strategies in the E/F/H-Class GTCC.Results show that EGR-IGV-FFC has considerable potential for the part-load performance enhancement and can show a higher combined cycle efficiency than IGV-FFC in the E-,F-,and H-Class GTCCs.However,the part-load performance improvement in the corresponding GTCC was weakened for the higher class of the gas turbine because of the narrower load range of EGR action and the deterioration of the gas turbine performance.Furthermore,EGR-IGV-FFC was inferior to IGV-FFC in improving the performance at loads below 50% for the H-Class GTCC.The results obtained in this paper could help guide the application of EGR-IGV-FFC to enhance the part-load performance of various classes of GTCC systems.

Tunnel personnel positioning method based on TOA and modified location-fingerprint positioning

To position personnel in mines, the study discussed in this paper built on the tunnel personnel positioning method on the basis of both TOA and location-finger print(LFP) positioning. Given non-line of sight(NLOS) time delay in signal transmission caused by facilities and equipment shielding in tunnels and TOA measurement errors in both LFP database data and real-time data, this paper puts forth a database data de-noising algorithm based on distance threshold limitation and modified mean filtering(MMF), as well as a real-time data suppression algorithm based on speed threshold limitation and MMF.On this basis, a nearest neighboring data matching algorithm based on historical location and the speed threshold limitation is used to estimate personnel location and realize accurate personnel positioning.The results from both simulation and the experiment suggest that: compared with the basic LFP positioning method and the method that only suppresses real-time data error, the tunnel personnel positioning methods based on TOA and modified LFP positioning permits effectively eliminating error in TOA measurement, making the measured data close to the true positional data, and dropping the positioning error:the maximal positioning error in measurements from experiment drops by 9 and 3 m, respectively, and the positioning accuracy of 3 m is achievable in the condition used in the experiment.

Survey on node deployment in wireless sensor networks

Node deployment strategy plays an important role in wireless sensor networks(WSNs)application because it determines the coverage,connectivity and network lifetime of WSNs.This paper reports the current research on the optimization means for achieving the desirable design goals in various applications.We categorize the placements strategies into are the static and the dynamic according to whether the node position change after the network is operational.The coverage,connectivity and energy consumption of WSNs are analysed and discussed in detail.

Underground Disease Detection Based on Cloud Computing and Attention Region Neural Network

Detecting the underground disease is very crucial for the roadbed health monitoring and maintenance of transport facilities,since it is very closely related to the structural health and reliability with the rapid development of road traffic.Ground penetrating radar(GPR)is widely used to detect road and underground diseases.However,it is still a challenging task due to data access anywhere,transmission security and data processing on cloud.Cloud computing can provide scalable and powerful technologies for large-scale storage,processing and dissemination of GPR data.Combined with cloud computing and radar detection technology,it is possible to locate the underground disease quickly and accurately.This paper deploys the framework of a ground disease detection system based on cloud computing and proposes an attention region convolution neural network for object detection in the GPR images.Experimental results of the precision and recall metrics show that the proposed approach is more efficient than traditional objection detection method in ground disease detection of cloud based system.

Off-Design Performance of 9F Gas Turbine Based on gPROMs and BP Neural Network Model

Gas turbines are increasingly and widely used,whose research and production reflect a country’s industrial capacity and level.Due to the changeable working environment,gas turbines usually work under the condition of simultaneous changes of ambient temperature,load and fuel.However,the current researches mainly focus on the change in single condition,and do not fully consider the simultaneous change in different conditions.On the basis of single condition,this paper further studies the dual off-design performance of gas turbines under three conditions:temperature-load,fuel-load and fuel-temperature.Firstly,the whole machine model of a gas turbine is established,in which the compressor model has the greatest impact on the performance of gas turbines.Therefore,this paper obtains a more accurate compressor model by combining the engineering modeling advantages of gPROMs and the powerful mathematical calculation ability of MATLAB neural network.Then,according to the established gas turbine model,the dual off-design performance is studied,which is mainly based on the parameter of output and efficiency.The result shows that the efficiency and power output of gas turbines will decrease with the increase of ambient temperature.With the decrease of fuel calorific value,power output and efficiency will increase.As the load decreases,the efficiency of the gas turbines will decrease,and these changes are consistent with the single off-design performance.However,when the fuel and temperature change simultaneously,only adjusting the IGV angle cannot avoid the surge when the temperature is above 30°C.At this time,it is necessary to adjust the extraction rate in order to ensure the safe and stable operation of gas turbines.Therefore,the research on dual off-design performance of gas turbines has an important significance for the peak shaving operation of gas turbines.