Integrated performance analysis and Prediction for a Tesla turbine in advanced adiabatic compressed air energy storage systems

The Tesla turbine provides a promising and more economical alternative for small-scale Compressed Air Energy Storage (CAES). However, due to significant differences in its structure compared to traditional turbines, it lacks comprehensive performance characterization in terms of thermodynamic performance, which has become an obstacle in the modeling and simulation process of this turbine in different thermal systems. In this study, an experimental rig was first built to characterize the Tesla turbine's performance, obtaining operating data under diverse conditions to serve as a validation benchmark. To overcome experimental limitations and extend the investigation to broader operating ranges, a three-dimensional CFD model was subsequently developed and validated, achieving a power prediction error of less than 4.3 %. Building on this, a novel correction factor was derived from experimental data to calibrate the simulation. This calibrated, high-fidelity dataset was then used to train a three-layer feedforward neural network. The resulting surrogate model predicts isentropic efficiency with high accuracy (R2 = 0.84, MAPE = 4.58 %) in milliseconds, achieving a speedup of over 107 times compared to the multi-hour ANSYS CFX baseline. The model successfully captured the critical thermo-mechanical coupling, revealing a thermal activation mechanism that enables high-efficiency operation in the high-parameter domain. By bridging the gap between high-fidelity physical characterization and computational efficiency, this work delivers a validated, dynamically responsive predictive tool, laying a robust foundation for system-level optimization and intelligent control of Tesla turbines in future AA-CAES applications.