Method of calculating the maximum achievable pressure of a liquid-vapor jet ejector using thermodynamic diagrams

The current landscape of energy production and industrial processes increasingly demands highly efficient and reliable equipment. Liquid-vapor jet ejectors (LVJEs), due to their simplicity, absence of moving parts, and ability to handle various working fluids, offer a compelling solution for a range of applications, including vacuum generation, refrigeration, and thermal compression. However, accurate design and optimization of these devices remain challenging due to complex thermodynamic interactions involved. Existing calculation methods often rely on simplified assumptions or empirical correlations, which may limit their applicability and accuracy across different operating conditions and working fluids.This work introduces a novel methodology for calculating LVJEs, enabling precise determination of thermodynamic pa-rameters at key points of their operating cycle through the application of enthalpy-entropy (h,s) diagrams. The proposed meth-odology yields reliable results for calculating LVJE operational parameters and deriving their fundamental geometric dimen-sions across various operating conditions. A significant advantage of this method is its inherent universality, allowing for its application with diverse working fluids and adaptability to a broad spectrum of input parameters.The practical utility of these findings lies in the potential for more accurate design of LVJEs for energy installations, thereby enhancing the efficiency and reliability of technological processes within these devices. Implementation of the devel-oped methodology is expected to significantly improve calculation accuracy and facilitate optimal LVJE design, representing a promising avenue for advancement in the field of power engineering. The subsequent phase of this research involves con-ducting experimental validations to correlate theoretical models with real-world outcomes. This will provide empirical support for the proposed methodology and refine its predictive capabilities.