This paper proposes a swarm intelligence based structural topology optimization method on cooling channels. This method employs the swarm intelligence concept to simulate thermal conduction phenomena in solid domain and heat exchange phenomena between fluid and solid domain. Research indicates that for cooling structures with chain-type channels, this method achieves optimization of intervals between channels; for cooling structures exhibiting grid-based channels, this method allows for optimized distribution of flow in different channels. The method was implemented on NetLogo, and the feasibility and accuracy of the simulation model were verified by commercial numerical software. This study show a prominent cost-effectiveness and new approach for topology optimization of internal cooling channels.

The design of a thermal cavity receiver and the arrangement of the fluid flow layout within it are critical in the construction of solar parabolic dish collectors, involving the prediction of the thermal–fluid physical field of the receiver and optimization design. However, the thermal–fluid analysis coupled with a heat loss model of the receiver is a non-linear and computationally intensive solving process that incurs high computational costs in the optimization procedure. To address this, we implement a net-based thermal–fluid model that incorporates heat loss analysis to describe the receiver’s flow and heat transfer processes, reducing computational costs. The physical field results of the net-based thermal–fluid model are compared with those of the numerical simulation, enabling us to verify the accuracy of the established thermal–fluid model. Additionally, based on the developed thermal–fluid model, a topology optimization method that employs a genetic algorithm (GA) is developed to design the cavity receiver and its built-in net-based flow channels. Using the established optimization method, single-objective and multi-objective optimization experiments are conducted under inhomogeneous heat flux conditions, with objectives including maximizing temperature uniformity and thermal efficiency, as well as minimizing the pressure drop. The results reveal varying topological characteristics for different optimization objectives. In comparison with the reference design (spiral channel) under the same conditions, the multi-objective optimization results exhibit superior comprehensive performance.

Nine methods currently available for estimation of fatigue properties from simple tensile data are discussed in detail. The estimation accuracies of these methods were compared according to the test data of nine types of steam turbine rotor steels. The results showed that the values calculated using the nine methods resulted in unacceptable values. In double logarithmic coordinates, the nine method functions were linearly modified, where the predicted lives were all less than quadruple the observed lives, except for the mixed method. A new method that takes the temperature influence into account is presented, which is suitable for fatigue life estimation of steam turbine rotor steels at high temperature (400 °C–600 °C). The estimation results show that the new method can provide acceptable accuracy.