No abstract

No abstract

The lightweight of axle hub has been recognized as a typical representative of reducing the quality of vehicles for energy saving and emission reduction. Therefore, a modeling analysis and lightweight design based on ANSYS for axle hub is proposed. Firstly, design dimensions and structural parameters of axle hub are modeled. The hub model is imported into workbench software to simulate the static analysis. The stress and deformation of the axle hub are obtained by mesh element division, boundary condition constraints and loading conditions, and the modal and fatigue strength of the hub are simulated to obtain the modal natural frequency and life data. Then, from the point of view of reducing the quality and safety of the hub, the structure of the hub is changed, the model is compared again, and the lightweight design scheme can be obtained. The results show that the weight of a hub decreases by 5.537% after lightening. This method can improve the structure, save materials, reduce production costs, and shorten the design cycle. This lightweight design method has important reference and practical significance for the lightweight design of axle wheels and similar products.

The flue gas desulfurization process in coal-fired power plants is energy and resource-intensive but the eco-efficiency of this process has scarcely been considered. Given the fluctuating unit load and complex desulfurization mechanism, optimizing the desulfurization system based on the traditional mechanistic model poses a great challenge. In this regard, the present study optimized the eco-efficiency from the perspective of operating data analysis. We formulated the issue of eco-efficiency improvement into a many-objective optimization problem. Considering the complexity between the system inputs and outputs and to further reduce the computational cost, we constructed a Kriging model and made a comparison between this model and the response surface methodology based on two accuracy metrics. This surrogate model was then incorporated into the NSGA-III algorithm to obtain the Pareto-optimal front. As this Pareto-optimal front provides multiple alternative operating options, we applied the TOPSIS to select the most appropriate alternative set of operating parameters. This approach was validated using the historical operation data from the desulfurization system at a coal-fired power plant in China with a 600 MW unit. The results indicated that the optimization would cause an improvement in the efficiency of desulfurization and energy efficiency but a slight increase in the consumption of limestone slurry. This study attempted to provide an effective operating strategy to enhance the eco-efficiency performance of desulfurization systems.

No abstract