Optimization Analysis of the Energy Management Strategy of the New Energy Hybrid 100% Low-Floor Tramcar Using a Genetic Algorithm

Decomposed Collaborative Modeling Approach for Probabilistic Fatigue Life Evaluation of Turbine Rotor

To improve simulation accuracy and efficiency of probabilistic fatigue life evaluation for turbine rotor, a decomposed collaborative modeling approach is presented. In this approach, the intelligent Kriging modeling (IKM) is firstly proposed by combining the Kriging model (KM) and an intelligent algorithm (named as dynamic multi-island genetic algorithm), to tackle the multi-modality issues for obtaining optimal Kriging parameters. Then, the decomposed collaborative IKM (DCIKM) comes up by fusing the IKM into decomposed collaborative (DC) strategy, to address the high-nonlinearity problems for accelerating simulation efficiency. Moreover, the DCIKM-based probabilistic fatigue life evaluation theory is introduced. The probabilistic fatigue life evaluation of turbine rotor is regarded as case study to verify the presented approach; the evaluation results reveal that the probabilistic fatigue life of turbine rotor is 3296 cycles. The plastic strain range ∆ε_p and fatigue strength coefficient σ_f′ are the main affecting factors to fatigue life, whose effect probability are 28% and 22%, respectively. By comparing with direct Monte Carlo method, KM method, IKM method and DC response surface method, the presented DCIKM is validated to hold high efficiency and accuracy in probabilistic fatigue life evaluation.

Shape Optimization of Single-Curvature Arch Dam Based on Sequential Kriging-Genetic Algorithm

Shape optimization of single-curvature arch dams using the finite element method (FEM) is often computationally expensive. To reduce the computational burden, this study introduces a new optimization method, combining a genetic algorithm with a sequential Kriging surrogate model (GA-SKSM), for determining the optimal shape of a single-curvature arch dam. At the start of genetic optimization, a KSM was constructed using a small sample set. In each iteration of optimization, the minimizing predictor criterion and low confidence bound criterion were used to collect samples from the domain of interest and accumulate them into a small sample set to update the KSM until the optimization process converged. A practical problem involving the optimization of a single-curvature arch dam was solved using the introduced GA-SKSM, and the performance of the method was compared with that of GA-KSM and GA-FEM methods. The results revealed that the GA-SKSM method required only 5.40% and 12.40% of the number of simulations required by the GA-FEM and GA-KSM methods, respectively. The GA-SKSM method can significantly improve computational efficiency and can serve as a reference for effective optimization of the design of single-curvature arch dams.

A Firefly Algorithm Optimization-Based Equivalent Consumption Minimization Strategy for Fuel Cell Hybrid Light Rail Vehicle

To coordinate multiple power sources properly, this paper presents an optimal control strategy for a fuel cell/battery/supercapacitor light rail vehicle. The proposed strategy, which uses the firefly algorithm to optimize the equivalent consumption minimization strategy, improves the drawback that the conventional equivalent consumption minimization strategy takes insufficient account of the global performance for the vehicle. Moreover, the strategy considers the difference between the two sets of optimized variables. The optimization objective is to minimize the daily operating cost of the vehicle, which includes the total fuel consumption, initial investment, and cycling costs of power sources. The selected case study is a 100% low-floor light rail vehicle. The advantages of the proposed strategy are investigated by comparison with the operating mode control, firefly algorithm-based operating mode control, and equivalent consumption minimization strategy. In contrast to other methods, the proposed strategy shows cost reductions of up to 39.62% (from operating mode control), 18.28% (from firefly algorithm-based operating mode control), and 13.81% (from equivalent consumption minimization strategy). In addition, the proposed strategy can reduce fuel consumption and increase the efficiency of the fuel cell system.