In this paper, a new method for three-phase equilibrium calculations at constant pressure and enthalpy (PH flash) is presented. Michelsen’s Q-function is derived from the Lagrangian of the constrained maximization of entropy with respect to mole numbers and temperature, subject to the enthalpy balance constraint. This saddle point problem is transformed into an unconstrained minimization one by using an augmented Q-function. The proposed hybrid algorithm consists of Newton iterations (with line search to ensure a decrease of the objective function) combined with a trust-region method when the Hessian matrix is not positive definite (that is, the Newton method fails to provide a descent direction). A small number of partial-Newton iterations (in which some partial derivatives are neglected in the Hessian) are performed in the early iteration stages. Based on Michelsen’s foundational work, we detail for the first time the implementation and results of the application of the corresponding hybrid algorithm to solve two- and three-phase isenthalpic flash problems by minimization of the augmented Q-function with respect to mole numbers and temperature, including analytical expressions of all required partial derivatives. We conduct extensive convergence tests within the P-H plane, using several representative fluids, including examples showing narrow-boiling behavior. The proposed calculation algorithm turns out to be robust and efficient. Convergence behavior is analyzed in detail for several selected conditions, including very difficult points. Convergence is obtained for the quasi-totality of test points; some outliers require the use of the extremely robust, but slower nested approach. The results demonstrate that the proposed hybrid algorithm is a strong candidate for robust and fast isenthalpic flash routines in complex compositional simulations (as for CO2 storage applications), considering two- and three-phase equilibria and including a narrow-boiling behavior.
Gas bubble accumulation limits mass transport in porous electrodes during alkaline water electrolysis at high current densities. Herein, synchrotron-based operando micro-CT and microstructure-resolved lattice Boltzmann method simulations are employed to unveil how porosity and geometric structure govern hydrogen bubble detachment and two-phase transport in alkaline water electrolysis. It is found that porous electrodes with a rationally designed ordered pore architecture enable efficient mass transport by minimizing gas trapping and promoting continuous electrolyte renewal. By contrast, commercial nickel foams with low porosity, despite their larger surface area, exhibit severe gas accumulation and poor electrode utilization. Guided by these insights, we 3D-printed a highly ordered square-grid electrode and, following catalyst deposition, achieved high-efficiency overall water splitting at 2 A cm-2 with a cell voltage of 2.13 V. This methodology, integrating operando micro-CT and lattice Boltzmann method simulations, delivers much-needed design rules for gas evolving porous electrodes and demonstrates that tuning a 3D pore architecture is critical for advanced alkaline water electrolysis.
We propose an analysis of the Quantum Phase Estimation (QPE) algorithm applied to electronic systems by investigating its free parameters such as the time step, number of phase qubits, initial state preparation, number of measurement shots, and parameters related to the unitary operators implementation. A deep understanding of these parameters is crucial to pave the way towards more automation of QPE applied to predictive computational chemistry and material science. To our knowledge, various aspects remain unexplored and a holistic parameter selection method remains to be developed. After reviewing key QPE features, we propose a constructive method to set the QPE free parameters. We derive, among other things, explicit conditions for achieving chemical accuracy in ground energy estimation. We also demonstrate that, using our conditions, the complexity of the Trotterized version of QPE tends to depend only on physical system properties and not on the number of phase qubits. Numerical simulations on the H2 molecule provide a first validation of our approach.
The deployment of Distributed Acoustic Sensing (DAS) for seismic monitoring has significantly increased in recent years due to its numerous advantages over conventional seismic sensors. DAS has the potential to play a crucial complementary role along with classical seismic networks, particularly in logistic challenging areas such as offshore and volcanic environments. However, DAS data are inherently noisier than seismometer data, primarily due to fiber coupling issues and optical noise associated with the instrument. As a result, effective denoising and signal enhancement techniques are essential to fully exploit the advantages of DAS data. Recent efforts in improving the quality of DAS data have primarily focused on denoising algorithms (mostly deep learning-based) aimed at reducing coherent and background noise. However, improvements in terms of signal-to-noise ratio can also be achieved through the application of characteristic functions to raw or pre-processed data. To date, the application of these methods to DAS data have been largely unexplored, with the exception of few standard algorithms. In this study, we investigate the signal enhancement capability on DAS data of a new characteristic function based on the hyperbolic cosine. More specifically, we assess the performance of this function in improving the signal-to-noise ratio and compare the results against a set of more standard characteristic functions. Our analysis follows a two-step approach. First, we quantify the signal enhancement achieved through the application of the different characteristic functions by computing the signal-to-noise ratio of the preprocessed DAS data. In the second step, we evaluate their capability to enhance signal coherence across all fiber channels. This is achieved through the application of a coherence-based detector, which provides an estimate of the coherence as a function of time. Following a standardized denoising procedure, we systematically evaluate the impact of each characteristic function in increasing both the signal-to-noise ratio and coherence of DAS data. We conduct our analysis both on synthetic data and on a real dataset of 947 events recorded at the Frontier Observatory for Research in Geothermal Energy (FORGE) site, in Utah, USA.
