The role of a pre-cooler is critical to the sCO₂-BC as it not only acts as a sink but also controls the conditions at the main compressor’s inlet that are vital to the cycle’s overall performance. Despite their prime importance, studies on the pre-cooler’s design are hard to find in the literature. This is partly due to the unavailability of data around the complex thermohydraulic characteristics linked with their operation close to the critical point. Henceforth, the current work deals with designing and optimizing pre-cooler by utilizing machine learning (ML), an in-house recuperator and pre-cooler design, an analysis code (RPDAC), and a cycle design point code (CDPC). Initially, data computed using 3D Reynolds averaged Navier-Stokes (RANS) equation is used to train the machine learning (ML) model based on the deep neural network (DNN) to predict Nusselt number ($Nu$) and friction factor ($f$). The trained ML model is then used in the pre-cooler design and optimization code (RPDAC) to generate various designs of the pre-cooler. Later, RPDAC was linked with the cycle design point code (CDPC) to understand the impact of various designs of the pre-cooler on the cycle’s performance. Finally, a multi-objective genetic algorithm was used to optimize the pre-cooler geometry in the environment of the power cycle. Results suggest that the trained ML model can approximate 99% of the data with 90% certainty in the pre-cooler’s operating regime. Cycle simulation results suggest that the cycle’s performance calculation can be misleading without considering the pre-cooler’s pumping power. Moreover, the optimization study indicates that the compressor’s inlet temperature ranging from 307.5 to 308.5 and pre-cooler channel’s Reynolds number ranging from 28,000 to 30,000 would be a good compromise between the cycle’s efficiency and the pre-cooler’s size.

The thermal-hydraulic behavior of supercritical water reactors with a parallel channel configuration was examined through a non-linear instability analysis. This analysis was performed under wide-ranging conditions and aspects, including different working supercritical fluids, varied heat-flux and flow-rate conditions, and channel inclinations. The supercritical fluid (SCFs) dynamics were captured using the density, enthalpy, and velocity analytical approximation functions. The major findings show that both SCFs (water and carbon dioxide) experienced density wave oscillations at a low pseudo-subcooling number. Static instability characteristics were observed for supercritical water, at a relatively high subcooling number. Further, it was found that at different heat flux, the hotter channel makes the overall system more unstable, whereas, at equal heat flux, parallel channels perform similar to a single-channel system. However, the effect of the inclination angle was found to be negligible owing to supercritical pressure conditions. Moreover, stable and unstable limit cycles along with out-of-phase oscillation characteristics were observed in dynamic stability regions. The present model was also compared with experimental and numerical data. Moreover, co-dimension and numerical simulations were performed to confirm the observed non-linear characteristics. This study helps to enhance the heat transfer characteristics during safe operation of heated channel systems, such as nuclear reactors and solar thermal systems.

In this paper, a novel nodalized reduced order model (NROM) has been developed to analyze the linear stability in a heated channel using supercritical water (SCW) as a coolant. The presented reduced order model is developed based on the two-phase flow system approach. The model is much simplified, which reduced the requirement of computational efforts and resources. In the heated channel, the SCW’s density shows a dramatic downfall near the pseudo-critical temperature, based on which it has been divided into n number of nodes. The one-dimension partial differentiation conservation equations of energy, mass and momentum are used and have been linearized by a small perturbation applied on its steady-state solution. These PDEs are converted into the corresponding time-dependent, nonlinear ordinary differential equations (ODEs) by using weighted residual method applied under some appropriate assumptions and approximations. These sets of ODEs (n+1 equations) are then solved analytically by using a state space approach to capture the stability boundary (SB) in terms of trans-pseudo-critical phase change number (N_tpc), pseudo-subcooling number (N_spc) by applying a constant external pressure drop (ΔP_tpc) condition across the channel. The NROM results are found to be in good agreement with the methodology and have been verified by numerical simulation. To extend this as a nonlinear stability analysis, the different types of the Hopf Bifurcation regime are also reported.