This study examines the predictive capability of deep learning method for adiabatic film cooling effectiveness distribution with variable operating conditions and geometric layouts. A conditional generative adversarial network is trained to establish nonlinear mapping from input to output. We embed the boundary condition information directly into the input tensor, thereby imparting the capability to address variable operating conditions. The processed input tensors include different blowing ratios M, incoming turbulence intensity Tu, geometry profile controlled by the inclination angle $\varphi$ , compound angle θ, number of hole rows n, pitch between hole rows P_x, and span-wise hole pitch P_y. The output data are adiabatic film cooling effectiveness fields generated by Reynolds-averaged Navier–Stokes simulations. The prediction results are in good agreement with the computational fluid dynamics results in terms of various statistical assessments. Furthermore, compared with conventional methods of solving Navier–Stokes equations, predictions based on deep learning result in better response times. Therefore, the method proposed in this study is of high significance in the early design of cooling structures for turbine blades.

The promise of silicon anodes for lithium‐ion batteries (LIBs), owing to the high theoretical capacity, is significantly affected by the huge volume changes (over 300%) during the lithiation/delithiation process. This leads to particle cracking during cycling impacting the electrode at multiple levels, from continued solid‐electrolyte interphase formation, particle contact losses, to delamination of the coating from the current collector. As a results, there is increased electrolyte consumption, and significantly shortened cycle life, hindering its commercial application. The problem is severe for commercially viable larger silicon microparticles (SiMPs > 1 µm). Herein, the cycling of 325 mesh (≤45 µm) SiMPs is successfully demonstrated using a composite binder consisting of inner graphene oxide coating and outside DNA polymer. The resultant hierarchical structure, with cycling‐induced 3D interconnection, maintains the electrodes integrity, as well as helps to stabilize the SEI. Thus, an excellent cycle life is observed, retaining a capacity of 808 mAh·g⁻¹ after 450 cycles at a current density of 840 mA·g⁻¹. This double protection strategy with a robust particle coating and a chemically bonded soft polymer offers an effective strategy for cyclability of SiMPs in practical LIBs.