A5031436628- Hydraulic Fracturing and Reservoir Analysis
- Turbomachinery Performance and Optimization
- Drilling and Well Engineering
- Technical Engine Diagnostics and Monitoring
- Reservoir Engineering and Simulation Methods
- Advanced Data Processing Techniques
- Seismic Imaging and Inversion Techniques
- Hydrocarbon exploration and reservoir analysis
- Advanced Sensor Technologies Research
- Advanced Combustion Engine Technologies
- High Temperature Alloys and Creep
- Advanced Multi-Objective Optimization Algorithms
- Rock Mechanics and Modeling
- Mining and Gasification Technologies
- Engineering Diagnostics and Reliability
- Rough Sets and Fuzzy Logic
- Fatigue and fracture mechanics
- Groundwater flow and contamination studies
- Advanced Aircraft Design and Technologies
- Adaptive Control of Nonlinear Systems
- Anomaly Detection Techniques and Applications
- Tunneling and Rock Mechanics
- Distributed Control Multi-Agent Systems
- Oil and Gas Production Techniques
- Robotic Path Planning Algorithms
China University of Petroleum, Beijing
2023-2025
Cranfield University
2010-2013
Accurate gas turbine performance models are crucial in many analysis and path diagnostic applications. With current thermodynamic modeling techniques, the accuracy of at off-design conditions is determined by engine component characteristic maps obtained rig tests these may not be available to users or accurate for individual engines. In this paper, a nonlinear multiple point adaptation approach using genetic algorithm introduced with aim improve prediction engines different calibrating...
At off-design conditions, engine performance model prediction accuracy depends largely on its component characteristic maps. With the absence of actual maps, adaptation needs to be done for good imitations performance. A non-linear multiple point Genetic Algorithm based developed earlier by authors using a set scaling factor functions has been proven capable making accurate over wide range operating conditions. However, success searching right coefficients heuristically, in order obtain...
Accurate and reliable component life prediction is crucial to ensure safety economics of gas turbine operations. In pursuit such improved accuracy reliability, model-based creep methods have become more complicated demand higher computational time. Therefore, there a need find an alternative approach that able provide quick solution for production engines while at the same time maintain reliability as methods. this paper, novel using artificial neural networks introduced accurate estimation...
Accurate gas turbine performance models are crucial in many analysis and path diagnostic applications. With current thermodynamic modelling techniques, the accuracy of at off-design conditions is determined by engine component characteristic maps obtained rig tests these may not be available to users or accurate for individual engines. In this paper, a non-linear multiple point adaptation approach using Genetic Algorithm introduced with aim improve prediction engines different calibrating...
Fault classification has become one of the main features in gas turbine health monitoring. Hence techniques such as path analysis, artificial neural networks, expert systems, fuzzy logic and many others have been developed for this purpose past. In paper, an alternative rough set based diagnostic method using enhanced fault signatures combined with three frameworks introduced, i.e. Framework 1 a single step to classify dual component faults, 2 first identify weather it is or faults second...
At off-design conditions, engine performance model prediction accuracy depends largely on its component characteristic maps. With the absence of actual maps, adaptation needs to be done for good imitations performance. A nonlinear multiple point genetic algorithm based developed earlier by authors using a set scaling factor functions has been proven capable making accurate predictions over wide range operating conditions. However, success searching right coefficients heuristically, in order...
<title>Abstract</title> Lithology identification of complex carbonate reservoirs is very important for fine characterization and quantitative evaluation reservoirs. In order to solve the problem reservoir lithologic logging response with strong multi solutions, this paper introduces deep forest algorithm deeply mine information from conventional calibrated by core descriptions, so as improve lithology accuracy reservoir. Deep a combination random neural network. It avoids shortage long...
Accurate and reliable component life prediction is crucial to ensure safety economics of gas turbine operations. In pursuit such improved accuracy reliability, model-based creep methods have become more complicated therefore demand computational time although they are flexible in applications, particular for new engines. Therefore, there a need find an alternative approach that able provide quick solution production engines while at the same maintain reliability as methods. this paper novel...