We propose a linear electrolyzer model for steady-state load flow analysis of multi-carrier energy networks, where the electrolyzer is capable of producing hydrogen gas and heat. For our electrolyzer model, we show that there are boundary conditions that lead to a well-posed problem. We derive these conditions for two cases, namely with a known and unknown heat efficiency parameter. Furthermore, the derived conditions are validated numerically. Moreover, we investigate the extensibility of our model by including nonlinear models from electricity, gas, and heat. In this setting, we derived boundary conditions based on our previous findings. Due to the involvement of nonlinearity, it is a challenge to prove that the boundary conditions lead to a well-posed problem. Therefore, we simulated the electrolyzer connected with an electricity, gas, and heat system. Additionally, we considered a known and unknown heat efficiency parameter. The numerical results support that the linear electrolyzer model is solvable in a multi-carrier energy network.

We present a matrix-free parallel scalable multilevel deflation preconditioned method for heterogeneous time-harmonic wave problems. Building on the higher-order deflation preconditioning proposed by Dwarka and Vuik (SIAM J. Sci. Comput. 42(2):A901-A928, 2020; J. Comput. Phys. 469:111327, 2022) for highly indefinite time-harmonic waves, we adapt these techniques for parallel implementation in the context of solving large-scale heterogeneous problems with minimal pollution error. Our proposed method integrates the Complex Shifted Laplacian preconditioner with deflation approaches. We employ higher-order deflation vectors and re-discretization schemes derived from the Galerkin coarsening approach for a matrix-free parallel implementation. We suggest a robust and efficient configuration of the matrix-free multilevel deflation method, which yields a close to wavenumber-independent convergence and good time efficiency. Numerical experiments demonstrate the effectiveness of our approach for increasingly complex model problems. The matrix-free implementation of the preconditioned Krylov subspace methods reduces memory consumption, and the parallel framework exhibits satisfactory parallel performance and weak parallel scalability. This work represents a significant step towards developing efficient, scalable, and parallel multilevel deflation preconditioning methods for large-scale real-world applications in wave propagation.

Large-scale geological storages of hydrogen (H₂) and carbon dioxide (CO₂) in saline aquifers present feasible options for a sustainable energy future. We compared the plume migration of CO₂ and H₂ in aquifers using the FluidFlower benchmark, incorporating the state-of-the-art thermophysical and petrophysical properties. The H₂ plume, with its higher buoyancy and mobility compared to CO₂, remains predominantly in the gas phase due to its lower solubility, increasing the chances of escaping through fractures or migration to distant regions. This additionally leads to a higher pressurized reservoir, which, along with higher buoyancy, increases the chance of caprock penetration. Dissolution trapping of CO₂ into brine increases over time due to its fingering, while H₂ does not show fingering. Our findings show that while geological carbon storage (GCS) benefits significantly from all structural, dissolution, and residual trapping, underground hydrogen storage (UHS) relies mainly on structural trapping, making the integrity of sealing elements of the system a key factor in its performance.

Background: Individuals who were formerly incarcerated have high tuberculosis incidence, but are generally not considered among the risk groups eligible for tuberculosis prevention. We investigated the potential health impact and cost-effectiveness of Mycobacterium tuberculosis infection screening and tuberculosis preventive treatment (TPT) for individuals who were formerly incarcerated in Brazil. Methods: Using published evidence for Brazil, we constructed a Markov state transition model estimating tuberculosis-related health outcomes and costs among individuals who were formerly incarcerated, by simulating transitions between health states over time. The analysis compared tuberculosis infection screening and TPT, to no screening, considering a combination of M tuberculosis infection tests and TPT regimens. We quantified health effects as reductions in tuberculosis cases, tuberculosis deaths, and disability-adjusted life-years (DALYs). We assessed costs from a tuberculosis programme perspective. We report intervention cost-effectiveness as the incremental costs per DALY averted, and tested how results changed across subgroups of the target population. Findings: Compared with no intervention, an intervention incorporating tuberculin skin testing and treatment with 3 months of isoniazid and rifapentine would avert 31 (95% uncertainty interval 14–56) lifetime tuberculosis cases and 4·1 (1·4–5·8) lifetime tuberculosis deaths per 1000 individuals, and cost US$242 per DALY averted. All test and regimen combinations were cost-effective compared with no screening. Younger age, longer incarceration, and more recent prison release were each associated with significantly greater health benefits and more favourable cost-effectiveness ratios, although the intervention was cost-effective for all subgroups examined. Interpretation: M tuberculosis infection screening and TPT for individuals who were formerly incarcerated appears cost-effective, and would provide valuable health gains. Funding: National Institutes of Health. Translation: For the Portuguese translation of the abstract see Supplementary Materials section.

Anderson acceleration (AA) has a long history of use and a strong recent interest due to its potential ability to dramatically improve the linear convergence of the fixed-point iteration. Most authors are simply using and analyzing the stationary version of Anderson acceleration (sAA) with a constant damping factor or without damping. Little attention has been paid to nonstationary algorithms. However, damping can be useful and is sometimes crucial for simulations in which the underlying fixed-point operator is not globally contractive. The role of this damping factor has not been fully understood. In the present work, we consider the non-stationary Anderson acceleration algorithm with optimized damping (AAoptD) in each iteration to further speed up linear and nonlinear iterations by applying one extra inexpensive optimization. We analyze the convergence rate this procedure and develop an efficient and inexpensive implementation scheme. We show by extensive numerical experiments that the proposed non-stationary Anderson acceleration with optimized damping procedure often converges much faster than stationary AA with constant damping, adaptive damping or without damping, especially in the cases larger window sizes are needed. We also observe that simple strategies like using constant damping factors and adaptive damping factors, sometimes, work very well for some problems while sometimes they are even worse than AA without damping. Our proposed method is usually more robust than AA with constant damping and adaptive damping. Moreover, we also observed from our numerical results that damping can be good, but choosing the wrong damping factors may slow down the convergence rate. Theoretical analysis of the effects of damping factors are needed and important.

This study explores the suitability of quasi-static pore-network modeling for simulating the transport of hydrogen in networks with box-shaped pores and square cylinder throats. The dynamic pore-network modeling results are compared with quasi-static pore-network modeling, and a good agreement is observed when the simulations reach steady-state, for a capillary number of N_c≤10⁻⁷. This finding suggests that the quasi-static approach can be used as a reliable and efficient method for studying hydrogen transport in similar networks.

We propose a matrix-free parallel two-level deflation method combined with the Complex Shifted Laplacian Preconditioner (CSLP) for two-dimensional heterogeneous Helmholtz problems encountered in seismic exploration, antennas, and medical imaging. These problems pose challenges in terms of accuracy and convergence due to scalability issues with numerical solvers. Motivated by the limitations imposed by excessive computational time and memory constraints when employing a sequential solver with constructed matrices, we parallelize the two-level deflation method without constructing any matrices. Our approach utilizes preconditioned Krylov subspace methods and approximates the CSLP preconditioner with a parallel geometric multigrid V-cycle. For the two-level deflation, standard inter-grid deflation vectors and further high-order deflation vectors are considered. As another main contribution, the matrix-free Galerkin coarsening approach and a novel re-discretization scheme as well as high-order finite-difference schemes on the coarse grid are studied to obtain wavenumber-independent convergence. The optimal settings for an efficient coarse-grid problem solver are investigated. Numerical experiments of model problems show that the wavenumber independence has been obtained for medium wavenumbers. The matrix-free parallel framework shows satisfactory weak and strong parallel scalability.

In this paper, we consider a block Jacobi preconditioner and various deflation techniques applied in the Deflated Preconditioned Conjugate Gradient (DPCG) method for solving a sparse system of linear equations derived from a statistical linear mixed model that analyses simultaneously phenotypic and pedigree information of genotyped and ungenotyped animals with Single Polymorphism Nucleotide genotypes of genotyped animals. In livestock production systems, evaluating the genetic merit of the animals through such a model is a key process to ensure an improvement of animals for some characteristics of interest at each generation. First, we propose to define the deflation vectors using a subdomain deflation approach that considers some biological properties of the genotypes. Using simulated data, this approach reduces the number of iterations by up to 87% in comparison to a Preconditioned Conjugate Gradient method with a Jacobi preconditioner. Furthermore, compared to a DPCG method with same number of subdomains but defined randomly, this approach reduces the number of iterations by up to 20% for the same computational costs of one DPCG iteration. The properties of the resulting systems show that this approach annihilates the largest eigenvalues of the preconditioned coefficient matrix. Second, we propose the use of solution vectors of 12 systems of equations that include between 0.25% and 3% less data, as deflation vectors. For reducing the computational costs, we also consider a Proper Orthogonal Decomposition-reduced set of these 12 vectors. The properties of the resulting systems show that this recycling information approach annihilates the smallest eigenvalues of the preconditioned coefficient matrix, and results in a reduction of up to 39% in comparison to the PCG method. Finally, based on our experiment, the combination of the subdomain deflation approach relying on biological properties and of the POD-based approach to recycle previous solution vectors, for defining the deflation vectors, results in annihilating both the smallest and largest eigenvalues, and in a reduction of up to 88 % of the number of iterations in comparison to the PCG method.