Research on Heat Exchange Law and Structural Design Optimization of Deep Buried Pipe Energy Piles

A deeply buried pipe energy pile (DBP-EP) combines the advantages of a ground source heat pump (GSHP) and an inside buried pipe energy pile (IBP-EP) and is an efficient, clean, and energy-saving technology. Based on field tests and numerical simulations, this paper explores the temperature distribution and heat exchange effects of DBP-EP under different influencing factors. The results show that when the pile-to-well ratio is approximately 0.3–0.4, the heat exchange of the energy pile obtains the best benefit; the inlet water temperature is the most significant factor affecting the heat exchange effect of the energy pile, and when combined with a reasonable pile-to-well ratio, the energy pile obtains the best heat exchange effect; the flow rate has a significant impact on the heat exchange effect of the energy pile, but needs to be set reasonably according to the pile-to-well ratio; the influence of inlet water temperature, well depth, flow rate, and pile length on the heat exchange efficiency of the energy pile is gradually weakened. The research results of this paper provide a theoretical basis for the structural design optimization of DBP-EP and promote the popularization and application of energy pile technology.

Efficient algorithms and models for mechanical and structural design optimization

In the present work, different algorithms and penalty methods for design optimization of mechanical elements and structures are applied. Seven robust optimization techniques and seven penalty methods are thoroughly investigated and implemented in MATLAB codes. In addition, different optimization models are compared using two benchmark problems, namely, the minimal cost design of a welded beam structure and the optimal buckling design of a functionally graded material column. A performance measure factor is defined to determine the best technique among the implemented optimization algorithms. The results are arranged and nested to make it easy for the reader to figure out each technique characteristics, and hence choose the suitable one for a specific design problem and/or application. Comprehensive computer experimentations were performed, and the best optimization techniques and models have been thoroughly demonstrated. The attained optimal solutions show that, in general, the hybrid algorithms worked better than the stand-alone ones and the sequential quadratic programming (SQP) with global search indicates a superior performance than other techniques. Finally, based on the present study, the adaptive and dynamic penalties need further investigation to become more consistent with the implemented optimization algorithms.