Interface-resolved simulations are essential for predicting and understanding boiling heat transfer phenomena. Such simulations generally come at a high computational cost, which continues to motivate the development of efficient frameworks. In recent years, conservative second-order phase field methods have gained popularity due to their efficient representation of phase interfaces. However, their potential for simulating complex boiling phenomena has not yet been explored. To address this gap, we develop a consistent and highly efficient framework suitable for simulating large-scale boiling flows. We derive a set of mixture equations to describe the two-phase flow. The mixture equations are coupled with the accurate conservative diffuse interface method [1] to capture the interface. We present additional terms in the momentum balance equation and demonstrate that the proposed momentum balance modifications are mandatory for accurately capturing phase-change-induced pressure jumps. To solve the set of equations, an alternative Fast Fourier Transform (FFT)-based pressure solution scheme is proposed. Additionally, a modified kinetic phase change model is utilized that does not involve calculating temperature gradients and avoids problem-dependent parameters. The framework is tested against a variety of benchmark simulations, both with and without phase change. Moreover, we achieve improved accuracy when simulating bubble dynamics without phase change at high density ratios. We show that the proposed FFT-based pressure solution scheme exhibits superior performance in calculating interfacial pressure jumps compared with a commonly used FFT solver. Regardless of phase change, more accurate startup behaviour is observed. In the presence of phase change, we are successful in removing interfacial pressure oscillations. Across all phase-change benchmark simulations, the new phase change model consistently provides reliable results. Finally, we successfully simulate the dynamics of bubbles in superheated liquid subjected to gravity and validate the results with experimental data.
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