Technology trends enable the integration of many processor cores in a System-on-Chip (SoC). In these complex architectures, several architectural parameters can be tuned to find the best trade-off in terms of multiple metrics such as energy and delay. The main goal of the MULTICUBE project consists of the definition of an automatic Design Space Exploration framework to support the design of next generation many-core architectures 1 .
Technology trends enable the integration of many processor cores in a System-on-Chip (SoC). In these complex architectures, several architectural parameters can be tuned to find the best trade-off in terms of multiple metrics such as energy and delay. The main goal of the MULTICUBE project consists of the definition of an automatic Design Space Exploration framework to support the design of next generation many-core architectures 1 .
To reduce the aerodynamic load of super high-speed elevators, in this paper, the coefficient of drag [Formula: see text] and the coefficient of yawing moment [Formula: see text] of the elevator are selected as optimization objectives for the optimization of the air rectification cover (ARC) shape. The elliptic curve method was used to build the parametric model of the ARCs, six design variables were selected, and the design space of the ARC was determined. With the optimal Latin hypercube design method, the training points were selected, and the computational fluid dynamics numerical simulation was conducted to calculate the corresponding responses. Then, the relationship between the design variables and the responses was analyzed. The radial basis function (RBF) surrogate model of the relationship between the design variables and responses was constructed. Finally, the non-dominated sorting genetic algorithm-II (NSGA-II) was employed to optimize the shape of the ARC. The results show that the [Formula: see text] and [Formula: see text] decrease by 16.51% and 60.92%, respectively, compared with the unoptimized ARC, indicating that the ARC designed in this paper is optimized and can effectively reduce the aerodynamic load. Furthermore, among all the design variables, the bluntness of the ARC in the [Formula: see text]-direction has the most significant effect on the aerodynamic load, and the height of the ARC ([Formula: see text] and [Formula: see text]) has the second most significant effect on the aerodynamic load of elevators.
In order to research the rescue path problem in the accident of passenger ships under tilt, this paper establishes a multi-objective rescue path optimization model under tilt effect. By analyzing the fuzzy time and fuzzy risk, the objective functions of this model are optimal satisfaction function and optimal risk function. Related constraints are also described mathematically. The PSO-GA (particle swarm and genetic) hybrid algorithm is used to solve the model when designing the algorithm. Two-level planning is incorporated in the algorithm, the best solution in the lower-level planning is assigned to the upper-level, and the upper-level plan feeds back the result to the lower level, and finally the global optimal Pareto solution is obtained. Decision makers can choose appropriate solutions based on their preference. The simulation experiment compares the multi-objective rescue path optimization model with the traditional time-optimal model. Among the three optimal solution sets, solution 1 decreases by 3.36% in risk and the satisfaction rate increases by 69.44%. Solution 2 rose by 13.96% in risk, but the satisfaction increased by 87.93%, and the risk of solution 3 decreased by 11.41%, while the satisfaction increased by 52.41%. The results show that the established model is reasonable and the algorithm is feasible.
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