Technical and economic modeling and evaluation of tesla turbine ORC driven by experimental data

While the Tesla turbine's structural simplicity and potential low cost make it an attractive expander for Organic Rankine Cycle (ORC) waste heat recovery, current assessments are significantly restricted by the reliance on air-based experimental data and immature economic modeling methodologies that fail to capture real-gas behaviors and the cost dynamics of modular design. To bridge these critical gaps, this paper presents a novel data-driven techno-economic assessment framework grounded in experimental validation using an organic working fluid (R245fa). Unlike traditional analytical approaches, we develop a neural network-based efficiency ( η _tur) prediction model directly from experimental data to capture non-linear characteristics and introduce a detailed cost model explicitly linking geometric parameters to manufacturing costs for MW-class applications. Subsequently, thermodynamic analysis, optimization, and economic comparison are performed for systems incorporating both Tesla and traditional expanders. Results indicate the turbine achieves an isentropic efficiency ( η _tur) of 59% under optimal conditions, with its manufacturing cost being over 90% lower than that of traditional turbines of an equivalent scale. Economic analysis reveals that for systems exceeding 9 MW, the Levelized Cost of Electricity (LCOE) of the Tesla turbine ORC (TORC) is lower than that of the traditional system (BORC), with an LCOE of 0.0158 $/(kW·h) at 37 MW, which is approximately 10% lower than that of BORC.