Due to the complex and harsh environmental factors, the useful life of the filter in the gas turbine air intake system is usually less than its design life. When the filter is seriously degraded, the power and thermal...
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Due to the complex and harsh environmental factors, the useful life of the filter in the gas turbine air intake system is usually less than its design life. When the filter is seriously degraded, the power and thermal efficiency of the gas turbine will decrease obviously due to the increase of inlet pressure loss. For evaluating the health condition of filters in the air intake system, this work forms a filter pressure loss model with the defined health index for the filter and five external environmental and control factors. By integrating the gas path component model, the combined model is applied in a real data set and the results show that (i) the proposed health index is efficient in representing the degradation state of the filter, (ii) the influencing factors on the pressure loss are successfully decoupled and their contributions on the pressure are quantitatively estimated, and (iii) the integrated model of filter pressure loss and gas path component can be used to better estimate the deterioration states of the filter as well as the gas turbine performance.
To improve gas turbine reliability and availability and prolong service life while reducing operation and maintenance costs, many gas path diagnostic methods based on steady state or quasi-steady state have been obtai...
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To improve gas turbine reliability and availability and prolong service life while reducing operation and maintenance costs, many gas path diagnostic methods based on steady state or quasi-steady state have been obtained, but no complete scientific system of gas-path diagnosis has been formed yet. Nowadays the operation of gas turbines needs to be more flexible in grid support mode, and the service life will be consumed faster than that during base load steady-state /quasi-steady-state operation under transient conditions. Therefore, it is urgent to solve the problem of optimal identification of component health parameters under transient conditions. Aiming at above problem, a novel gas path diagnostic method is proposed. Firstly, an equivalent cooling flow processing method is proposed for thermodynamic modeling for gas path diagnostic purpose. Secondly, a steady state performance model based diagnostic scheme under transient conditions based on local optimization algorithm is proposed. At last the diagnostic performance with Newton-Raphson algorithm and Kalman filter algorithm as local optimization algorithm is comparatively analyzed. The case studies have showed that the proposed diagnostic method can be effectively utilized to detect both component gradual failure and abrupt fault quantitatively and the former has better real-time performance and diagnostic accuracy under transient conditions.
There are plenty of intelligence algorithms and heuristic algorithms for TSP (Traveling Salesman Problem). In this paper, local optimization algorithm which is a good representative of heuristic algorithms was analyze...
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ISBN:
(纸本)9783037859391
There are plenty of intelligence algorithms and heuristic algorithms for TSP (Traveling Salesman Problem). In this paper, local optimization algorithm which is a good representative of heuristic algorithms was analyzed. The performance of 2-opt (optimization), 3-opt and 4-opt were analyzed and compared through experiments. To reduce running time and improve their feasibility, a modification was made on 3-opt and 4-opt. Ant colony optimization as a good representative of intelligence algorithm was combined with k-opt to analyze. The results provide reference to application of k-opt and designing optimizationalgorithms for TSP in future.
Open vehicle routing problem is a kind of special vehicle routing problem, in which the vehicles do not return the depots after completing the task. Aiming at open vehicle routing problem, the mathematical model was f...
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ISBN:
(纸本)087849278X
Open vehicle routing problem is a kind of special vehicle routing problem, in which the vehicles do not return the depots after completing the task. Aiming at open vehicle routing problem, the mathematical model was founded by introducing virtual depots. A quantum evolutionary algorithm combined with local optimization algorithms was proposed in this paper, in which 0-1 matrix encoding was used to construct chromosomes, rotation gate with adaptively adjusting rotation angle was used to realize evolution, nearest neighbors and 2-Opt were incorporated to further improve solutions. Based on benchmark problems, the algorithm's parameters were discussed, and the computation result was compared to those of other algorithms. The Computation results indicated that the proposed algorithm was an efficient method for solving open vehicle routing problem.
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