Mesh Grid Research Articles

Fast communication-aided protection scheme for half-bridge MMC-based Multi-terminal HVDC grids

The protection of half-bridge modular multilevel converter-based (MMC) HVDC grids demands balancing selectivity, sensitivity, and robustness while ensuring fast operation. However, existing single-ended protection schemes often struggle with balancing selectivity and sensitivity, while double-ended schemes face challenges in achieving both speed and sensitivity. This study investigates the theoretical characteristics of first fault current traveling waves (FFCTWs) as they propagate along the faulted and non-faulted lines within a grid. Theoretical analysis reveals unique mode-1 FFCTW characteristics at faulted line ends over that of non-faulted ones, overcoming the shortcomings in methods based on the similarity of FFCTWs in ring grids. Protection criteria implementation utilizes directional morphological features of mode-1 currents at the line ends and of local poles’ currents for fault discrimination and classification, respectively. The proposed method avoids certain conditions like strong boundary features or line parameters to be applicable and can operate effectively even under measurement anomalies. To validate the proposed protection scheme, a meshed MMCHVDC grid is modeled using the PSCAD software. Simulation studies verify the selectivity and sensitivity of the scheme in case of various types of low and high-impedance faults, as well as robustness against non-fault operating conditions. Moreover, the applicability of the proposed method is assessed as primary protection and in comparison to state-of-the-art protection methods.

High gravimetric energy density lead acid battery with titanium-based negative grids employing expanded mesh sandwich structure

Addressing the low gravimetric energy density issue caused by the heavy grid mass and poor active material utilization, a titanium-based, sandwich-structured expanded mesh grid (Ti/Cu/Pb) for lead-acid battery negative electrode is introduced. Titanium was chosen for its advantageous properties such as low density, high mechanical strength, and good electrical conductivity, which reduces the electrode mass and enhances battery gravimetric energy density. However, titanium's use in battery negative grids is limited due to its passivation in sulfuric acid and poor adhesion to the active material. To overcome these drawbacks, a copper layer is added to prevent passivation, and a lead layer is applied to improve the adhesion between the titanium matrix and the active material. This innovative Ti/Cu/Pb negative grid reduces electrode mass and increases current density, boosting active material utilization. Electrode with Ti/Cu/Pb negative grid achieves an gravimetric energy density of up to 163.5 Wh/kg, a 26 % increase over conventional lead-alloy electrode. With Ti/Cu/Pb negative grid, battery cycle life extends to 339 cycles under a 0.5C 100 % depth of discharge, marking a significant advance over existing lightweight negative grid batteries. This research not only demonstrates a significant step in lead-acid battery enhancement but also proposes a methodological approach for future high gravimetric energy density battery design.

Study on aerodynamic and coupled motion response characteristics of floating vertical axis wind turbine

ABSTRACT To improve the efficiency of a floating VAWT (FVAWT), the aerodynamic and coupled motion response characteristics of 5MW Φ-type FVAWT are studied in fully coupled system. Based on the computational fluid dynamics (CFD) method, the dynamic fluid body interaction (DFBI), overlapping grid and dynamic mesh technique are adopted for numerical simulations. Under wind-wave coupling conditions, the power coefficient (Cp) and motion response of FVAWTs with different tip speed ratios (TSR) and height-todiameter ratios (η) are analysed. The mechanism of platform heave motion on torque Q generation of VAWT is explored. The results show that the FVAWT with height-to-diameter ratio of 1.2 and tip speed ratio of 5 has optimal aerodynamic and motion performance. Under the optimal tip speed ratio, the Cp of the offshore FVAWT is 5.21% higher than that of the land VAWT. As the floating platform rises, the torque of wind turbine decreased correspondingly, and vice versa.

Neutron and photon calculations of beam shaping assembly for BNCT based on Monte Carlo-discrete ordinates coupled method

The radiation field parameters at the beam shaping assembly (BSA) exit port are the key to boron neutron capture therapy (BNCT) treatment. However, obtaining calculation results with high efficiency and accuracy using only Monte Carlo (MC) method or discrete ordinates (SN) method is difficult. Based on NECP-MCX code and Marvin code, this paper implemented the Monte Carlo-discrete ordinates coupled method and applied this method to the calculation of the radiation field at the BSA exit port. The effect of mesh grid division, the order of quadrature set, and the bias of quadrature set on the results of the coupled calculation was studied. It was concluded that the order and bias of the quadrature set have not improved the coupled calculation results. It was found that the data library used in the SN calculation is the main cause. Therefore, the Monte Carlo-discrete ordinates coupled method can be quickly and accurately applied to the calculation of the radiation field at the BSA exit port by dividing the energy and coordinate intervals rationally and using a more suitable cross section library.

Research on Sediment Deposition Characteristics and the Vegetation Restoration of Ecological Riverbanks in the Deep Waterway Regulation Scheme of Yangtze River

In order to explore the sediment deposition characteristic of ecological riverbanks associated with vegetation restoration in the deep waterway regulation scheme of Yangtze River, two kinds of typical ecological riverbanks and a traditional riprap riverbank (TRR) in engineering areas were investigated. The vegetation community characteristics, sediment aggregate compositions, nutrient contents, total organic carbon (TOC), sediment microbial biomass carbon (MBC), sediment microbial biomass nitrogen (MBN), and sediment microbial biomass phosphorus (MBP) were determined. The results indicated that the ecological restoration effect of the lattice gabion ballasted vegetation mat riverbank (LGBVR) was best, followed by the mesh grid riverbank (MGR), and that of the TRR was relatively poor. In different ecological riverbanks, the sediment aggregated compositions were not significantly varied. The sediment contents of NH4+-N, available phosphorus (AP), and TOC in ecological riverbank areas were relatively higher than those of the TRR. In the LGBVR, the sediment contents of MBC were relatively higher than those of the others. The sediment deposition characteristics and ecological restoration effects in the study area should be monitored for a long time.

Bayesian-optimized LSTM-DWT approach for reliable fault detection in MMC-based HVDC systems

As Europe integrates more renewable energy resources, notably offshore wind power, into its super meshed grid, the demand for reliable long-distance High Voltage Direct Current (HVDC) transmission systems has surged. This paper addresses the intricacies of HVDC systems built upon Modular Multi-Level Converters (MMCs), especially concerning the rapid rise of DC fault currents. We propose a novel fault identification and classification for DC transmission lines only by employing Long Short-Term Memory (LSTM) networks integrated with Discrete Wavelet Transform (DWT) for feature extraction. Our LSTM-based algorithm operates effectively under challenging environmental conditions, ensuring high fault resistance detection. A unique three-level relay system with multiple time windows (1 ms, 1.5 ms, and 2 ms) ensures accurate fault detection over large distances. Bayesian Optimization is employed for hyperparameter tuning, streamlining the model’s training process. The study shows that our proposed framework exhibits 100% resilience against external faults and disturbances, achieving an average recognition accuracy rate of 99.04% in diverse testing scenarios. Unlike traditional schemes that rely on multiple manual thresholds, our approach utilizes a single intelligently tuned model to detect faults up to 480 ohms, enhancing the efficiency and robustness of DC grid protection.

Abnormal behavior of the plasma potential in an inductively coupled plasma with a DC-biased grid

Abstract The formation of the plasma potential and the generation mechanism of very low electron temperature plasma are investigated in an inductively coupled plasma with a DC biased grid. The electron temperature is controlled from 2.4 eV to 0.2 eV according to the grid voltage (10 V to −40 V). Interestingly, when the grid voltage is negatively biased, the electron temperature decreases and the plasma potential decreases with the grid voltage, but then increases below −10 V which is abnormal. This behavior of the plasma potential is abnormal since the plasma potential is generally proportional to the electron temperature. The main reason for the abnormal increase of the plasma potential is the difference in the flux of electrons and ions below the grid. As the grid is negatively biased, the electron flux is greatly reduced compared to the ion flux, leading to an increase in plasma potential. After −20 V, the plasma potential saturates, because although the number of electrons entering the grid decreases, the electron flux is maintained by secondary electrons generated in the grid mesh. This abnormal increase in plasma potential decreases with pressure. An increase in plasma potential with gas species is also observed. The same behavior is observed for Ar, He, and N2 gases. The abnormal behavior of the plasma potential is explained with the current continuity.

Data-driven hybrid modelling of waves at mid-frequencies range: Application to forward and inverse Helmholtz problems

In this paper, we introduce a novel hybrid approach that leverages both data and numerical simulations to address the challenges of solving forward and inverse wave problems, particularly in the mid-frequency range. Our method is tailored for efficiency and accuracy, considering the computationally intensive nature of these problems, which arise from the need for refined mesh grids and a high number of degrees of freedom. Our approach unfolds in multiple stages, each targeting a specific frequency range. Initially, we decompose the wave field into a grid of finely resolved points, designed to capture the intricate details at various wavenumbers within the frequency range of interest. Importantly, the distribution of these grid points remains consistent across different wavenumbers. Subsequently, we generate a substantial dataset comprising 1,000 maps covering the entire frequency range. Creating such a dataset, especially at higher frequencies, can pose a significant computational challenge. To tackle this, we employ a highly efficient enrichment-based finite element method, ensuring the dataset’s creation is computationally manageable. The dataset which encompasses 1000 different values of the wavenumbers with their corresponding wave simulation will be the basis to train a fully connected neural network. In the forward problem the neural network is trained such that a wave pattern is predicted for each value of the wavenumber. To address the inverse problem while upholding stability, we introduce latent variables to reduce the number of physical parameters. Our trained deep network undergoes rigorous testing for both forward and inverse problems, enabling a direct comparison between predicted solutions and their original counterparts. Once the network is trained, it becomes a powerful tool for accurately solving wave problems in a fraction of the CPU time required by alternative methods. Notably, our approach is supervised, as it relies on a dataset generated through the enriched finite element method, and hyperparameter tuning is carried out for both the forward and inverse networks.

Finite element solution of coupled multiphysics reaction-diffusion equations for fracture healing in hard biological tissues

This study proposes a computational framework to investigate the multi-stage process of fracture healing in hard tissues, e.g., long bone, based on the mathematical Bailon-Plaza and Van der Meulen formulation. The goal is to explore the influence of critical biological factors by employing the finite element method for more realistic configurations. The model integrates a set of variables, including cell densities, growth factors, and extracellular matrix contents, managed by a coupled system of partial differential equations. A weak finite element formulation is introduced to enhance the numerical robustness for coarser mesh grids, complex geometries, and more accurate boundary conditions. This formulation is less sensitive to mesh quality and converges smoothly with mesh refinement, exhibiting superior numerical stability compared to previously available strong-form solutions. The model accurately reproduces various stages of healing, including soft cartilage callus formation, endochondral and intramembranous ossification, and hard bony callus development for various sizes of fracture gap. Model predictions align with the existing research and are logically coherent with the available experimental data. The developed multiphysics simulation clarifies the coordination of cellular dynamics, extracellular matrix alterations, and signaling growth factors during fracture healing.

Lidar DEM and Computational Mesh Grid Resolutions Modify Roughness in 2D Hydrodynamic Models

AbstractTopography and the computational mesh grid are fundamental inputs to all two‐dimensional (2D) hydrodynamic models, however their resolutions are often arbitrarily selected based on data availability. With the increasing use of drone technology, the end user can collect topographic data down to centimeter‐scale resolution. With this advancement comes the responsibility of choosing a resolution. In this study, we investigated how the choice of mesh grid and digital elevation model (DEM) resolutions affect 2D hydrodynamic modeling results, specifically water depths, velocities, and inundation extent. We made pairwise comparisons between simulations from a 2D HEC‐RAS model with varying mesh grid resolutions (1 and 2 m) and drone‐based lidar DEM resolutions (0.1, 0.25, 0.5, 1, and 2 m) over a 1.5 km reach of Stroubles Creek in Blacksburg, Virginia. The model was rerun for up to ±4% change in floodplain roughness to determine how the DEM and mesh grid changes relate to an equivalent change in roughness. We found that the modeled differences from resolution change were equivalent to altering floodplain roughness by up to 12% for depths and 44% for velocities. The largest differences in velocity were concentrated at the channel‐floodplain interface, whereas differences in depth occurred laterally throughout the floodplain and were not correlated with lidar ground point density. We also found that the inundation boundary is dependent on the DEM resolution. Our results suggest that modelers should carefully consider what resolution best represents the terrain while also resolving important riparian topographic features.

Data-driven methods for the inverse problem of suspension system excited by jump and diffusion stochastic track excitation

Following a lengthy tenure of service, suspension systems may exhibit inaccurate model parameters due to the presence of complex conditions, which can result in a deterioration of control and the potential for operational failures. It is therefore imperative to estimate the key parameters of suspension systems. This paper introduces two data-driven methods for addressing the inverse problem in suspension systems subjected to stochastic track excitations. By employing physics-informed neural networks (PINNs) and Monte Carlo (MC) simulation, we are able to address the resulting integro-differential equation that arises from stochastic jump processes, thus avoiding the necessity for mesh grids. In order to mitigate the numerical challenges that arise from the system parameters, a residual-based adaptive sampling method is proposed. These methods effectively infer unknown parameters, addressing scenarios where data is directly available as a probability density function (PDF) or only sparse trajectories are given. In the latter case, a novel loss function employing Kullback-Leibler divergence facilitates learning from stochastic trajectories. Both methods successfully obtain solutions to the forward Kolmogorov equation, as validated by numerical experiments testing their robustness against added noise. The results demonstrate accurate parameter estimation under varying noise intensities, highlighting the methods’ robustness.

Postoperative facial prediction for mandibular defect based on surface mesh deformation

Objectives: This study aims to introduce a novel predictive model for the post-operative facial contours of patients with mandibular defect, addressing limitations in current methodologies that fail to preserve geometric features and lack interpretability.Methods: Utilizing surface mesh theory and deep learning, our model diverges from traditional point cloud approaches by employing surface triangular mesh grids. We extract latent variables using a Mesh Convolutional Restricted Boltzmann Machines (MCRBM) model to generate a three-dimensional deformation field, aiming to enhance geometric information preservation and interpretability.Results: Experimental evaluations of our model demonstrate a prediction accuracy of 91.2 %, which represents a significant improvement over traditional machine learning-based methods.Conclusions: The proposed model offers a promising new tool for pre-operative planning in oral and maxillofacial surgery. It significantly enhances the accuracy of post-operative facial contour predictions for mandibular defect reconstructions, providing substantial advancements over previous approaches.

PM2D: A parallel GPU-based code for the kinetic simulation of laser plasma instabilities at large scales

Laser plasma instabilities (LPIs) have significant influences on the laser energy deposition efficiency and therefore are important processes in inertial confined fusion (ICF). Numerical simulations play important roles in revealing the complex physics of LPIs. Since LPIs are typically a three wave coupling process, the precise simulations of LPIs with kinetic effects require to resolve the laser period (around one femtosecond) and laser wavelength (less than one micron). In the typical ICF experiments, however, LPIs are involved in a spatial scale of several millimeters and a temporal scale of several nanoseconds. Therefore, the precise kinetic simulations of LPIs in such scales require huge computational resources and are hard to be carried out by present kinetic codes like particle-in-cell (PIC) codes. In this paper, a full wave fluid model of LPIs is constructed and numerically solved by the particle-mesh method, where the plasma is described by macro particles that can move across the mesh grids freely. Based upon this model, a two-dimensional (2D) GPU code named PM2D is developed. The PM2D code can simulate the kinetic effects of LPIs self-consistently as normal PIC codes. Moreover, as the physical model adopted in the PM2D code is specifically constructed for LPIs, the required macro particles per grid in the simulations can be largely reduced and thus overall simulation cost is considerably reduced comparing with PIC codes. More importantly, the numerical noise in the PM2D code is much lower, which makes it more robust than PIC codes in the simulation of LPIs for the long-time scale above 10 picoseconds. After the distributed computing is realized, our PM2D code is able to run on GPU clusters with a total mesh grids up to several billions, which meets the requirements for the simulations of LPIs at ICF experimental scale with reasonable cost. Program summaryProgram Title: PM2DCPC Library link to program files:https://doi.org/10.17632/xscj6vnkkw.1Licensing provisions: GNU General Public License v3.0.Programming language: C++, CUDA.Nature of problem: Although the large scale simulations of laser plasma instabilities (LPIs) is of great significance for the inertial confinement fusion (ICF), there is still no suitable code to simulate these problems. PM2D code based on a GPU platform provides an effective method to simulate these large scale problems in ICF.Solution method: A fluid model for LPIs is established firstly, which contains wave equations that describe the laser propagating process, electron and ion fluid equations that describe the plasma motions, and a Poisson's equation that describes the electrostatic field induced by charge separation. The wave equation is solved on a rectangular region using absorption boundary conditions on all of four boundaries. The absorption boundary condition on the left boundary is further extended to allow the incidence of driven lasers and absorption of scattering lasers simultaneously. The fluid equations in the physical model are solved by the particle-mesh method, in which the macro particles are driven to move by fluid forces. Since macro particles can move freely within the fixed fluid grids, the PM2D code can capture the kinetic effects self-consistently. The Poisson's equation for the electrostatic field is solved by a Fourier decomposition method in the y direction, which helps to decrease the simulation cost greatly. The PM2D code is developed on a GPU platform base on CUDA toolkit, which largely increase the computational speed.

EVALUATING ROUTING ALGORITHMS ACROSS DIFFERENT WIRELESS MESH NETWORK TOPOLOGIES USING NS-3 SIMULATOR

Wireless Mesh Networks are gaining traction as a solution for delivering reliable connectivity without centralized infrastructure. They operate through wireless node interconnections, forming self-configuring networks ideal for scenarios where wired networks are impractical. Routing is crucial in Wireless Mesh Networks to ensure efficient communication among nodes. However, the suitability of routing algorithms for Wireless Mesh Network’s topology requires further investigation. This paper proposes an investigation into the effectiveness of routing algorithms like AODV, DSDV, and OLSR across various Wireless Mesh Networks topologies using NS-3 simulation. It also aims to determine the optimal number of nodes and protocols to maximize throughput and minimize packet loss within a limited area. Through rigorous NS-3 simulations, the study demonstrates that AODV, DSDV, and OLSR exhibit differing effectiveness across random, mesh grid, and Fruchterman-Reingold topologies. These results emphasize the importance of considering topology-specific factors when selecting and optimizing routing protocols for Wireless Mesh Networks. In summary, Wireless Mesh Networks offer decentralized connectivity, but the effectiveness of routing algorithms in different topologies remains understudied. This investigation addresses this gap by evaluating routing algorithms across various topologies, shedding light on their suitability and performance in Wireless Mesh Networks.

Uniaxial tensile behavior of engineering cementitious composite reinforced with fiber textile grid and steel wire mesh

The Textile Reinforced Engineering Cementitious Composite (TRECC) is a cementitious composite material reinforced with short polyvinyl alcohol (PVA) or polyethylene (PE) fibers, possessing exceptional ductility. This unique attribute enhances the utilization of the textile grid’s strength and significantly improves the load-bearing and deformation capabilities of ECC members reinforced with textile grids. In this study, the effects of fiber textile grids and steel wire mesh on the tensile properties of ECC were investigated. The test results showed that the carbon textile grid significantly improved the peak strength of specimens. On the other hand, the glass textile grid significantly improved the ultimate strain of specimens. The carbon combined with glass textile grids can integrate effectively the advantages of both carbon and glass textile grids. When compared with single textile grids, the combination uses of steel wire mesh and fiber textile grids resulted in a significant improvement in ultimate strain and post-peak energy absorption values. Finally, based on the test data, this study proposed the peak strength calculation of TRECC and provided the stress–strain expressions of the initial non-linear strain hardening phase and the tensile softening phase and a related analytical model to describe the stress–strain behavior of TRECC.

Analyses of hydrogen local environments in metals and intermetallic compounds using inelastic neutron scattering calculations based on first-principles hydrogen adiabatic potentials

This study re-evaluates the theoretical approach to analyzing inelastic neutron spectra of hydrogen-containing metals and intermetallic compounds. Previously, these analyses utilized hydrogen quantum nuclear states, modeled as solutions to the Schrödinger equation. The potential surfaces in these models were approximated from the total energies derived from first-principles electronic structure calculations. The current study improves upon this method by employing more efficient and accurate treatments for sampling the potential surface. It utilizes symmetrically irreducible sampling points arranged on densely populated mesh grids for the first-principles calculations. A comparative analysis of the theoretical predictions with experimental spectra for hydrides of Ti2Sb and Ti3Sb, as well as a LaNi5 hydrogen primary solid solution, demonstrates that this approach is promising for elucidating the unknown local environments of hydrogen atoms in systems where the approximate potential well describes the hydrogen quantum states.

Assessment of Gravity Waves From Tropopause to Thermosphere and Ionosphere in High‐Resolution WACCM‐X Simulations

AbstractA new version of NCAR Whole Atmosphere Community Climate Model with thermosphere/ionosphere extension (WACCM‐X) has been developed. The main feature of this version is the species‐dependent spectral element (SE) dynamical core solved on a cubed sphere grid, eliminating the polar singularity and enabling simulations at high‐resolutions. Molecular viscosity and diffusion in the horizontal direction are also included. The Conservative Semi‐Lagrangian Multi‐Tracer Transport Scheme (CSLAM) is employed for the species transport. An efficient regridding scheme based on the Earth System Modeling Framework is used to map fields between the physics mesh and geomagnetic grid. Simulations have been performed at coarse (∼200 km and 0.25 scale height) and high (∼25 km and 0.1 scale height) resolutions. Spatial distribution of the resolved gravity waves from the high‐resolution simulations compares well with available observations in the middle and upper atmosphere. Analysis of the scale dependence of the gravity wave energy density and momentum flux shows that, while larger scale waves are dominant energetically at most latitudes, smaller scale waves contribute significantly to the total momentum flux, especially at mid‐high latitudes. The waves in the thermosphere are shown to be strongly modulated by the large‐scale wind through Doppler shift and molecular damping, and they cause large neutral atmosphere and plasma perturbations.

On the phase field based model for the crystalline transition and nucleation within the Lagrange multiplier framework

Understanding the complexity of the nucleation and transition between the crystalline and quasicrystalline is significant because the structural incommensurability is anisotropic and of significance in revealing material properties. This paper reports the two- and three-dimensional nucleation and transition investigation from quasicrystals to crystals using a phase field method. The investigation starts with the Lifshitz–Petrich (LP) model, which is derived from the Landau theory for the exploration of critical nuclei. By the variational derivation, we construct two phase field models with tenth- and eighth-order, which provide the possibility of exploring the transition under stable phase states. In order to dissipate the original Lifshitz–Petrich energy, we apply a Lagrange multiplier method to modify the two models and solve them by the Fourier spectral method. Whereas the nonlinearity leads to expensive computational burden and extra stiffness, the designed algorithm can effectively avoid the numerical oscillations caused by rigidity and keep an O(Nlog⁡N) computational complexity, where N is the mesh grid size. To further demonstrate the robustness and advantages of the proposed method for handling phase-field modeling of crystalline structures, we compare its performance with other methods for constructing unconditionally stable methods. Our method can be directly implemented on a GPU for acceleration and achieves multiple times faster performance compared to CPU-only alternatives. Various numerical tests have been used to validate that our method works well for revealing the transition between different stable states during the nucleation process.

On the Application of the Nyström Method to Two-Dimensional Neutral Particle Transport Problems in Heterogeneous Media

Over time, several methods were developed to deal with neutral particle transport problems. The interest in these problems is related to their wide range of applications, from neutron transport and heat transfer in nuclear reactors to radiative transfer in atmospheric clouds. Unlike the discrete ordinates or discrete ordinates–like methods, integral methods do not require discretization of angular variables. Instead, angular variables are completely eliminated by an integration procedure over the solid angle, which allows elimination of the ray effect. That said, this paper presents a new approach to estimate the scalar flux in two-dimensional fixed-source neutron transport problems in a heterogeneous medium, considering isotropic scattering and vacuum and reflective boundary conditions. Here, the Nyström method is combined with the singularity-subtraction technique to present an integral formulation for the scalar flux in a mesh grid over all regions of the domain. The iterative method of the Neumann series is used as an alternative to direct methods to solve the resulting system of equations generated from the domain discretization. Numerical results are given to verify the offered method.

Towards a cutting‐edge metallization process for silicon heterojunction solar cells with very low silver laydown

AbstractWithin this work, we investigate the potential to optimize the screen‐printed front side metallization of silicon heterojunction (SHJ) solar cells. Three iterative experiments are conducted to evaluate the impact of the utilized fine mesh screen configurations and grid layout adaption (finger pitch) for the front side metallization on silver laydown and electrical performance of the solar cells. With respect to the screen configuration, we compare the performance of a fine‐mesh knotless screen to a conventionally angled screen demonstrating an additional gain of Δη = +0.1%abs due to reduced shading losses. Additionally, a grid layout is improved by increasing the number of contact fingers from 120 to 156. Furthermore, the current possibility to push the fine‐line printing process for low‐temperature pastes to the limit is investigated by reducing the nominal finger width wn to 20, 18, and 15 μm. It is shown that even the smallest nominal width of wn = 15 μm can be printed with high quality, leading to an additional efficiency gain of Δη = +0.15%abs as well as a reduction of silver paste laydown by −5 mg. Finally, a batch of champion cells is fabricated by applying the findings of the previous experiments, which results in a maximum efficiency of ηmax = 23.2%. Compared to the reference group without optimization, this corresponds to a gain of Δη = +0.17%abs, which comes along with an additional decrease of the silver paste laydown by approximately −2 mg. This emphasizes the significance of consistent optimization of the screen‐printing process in terms of cell performance and resource utilization for SHJ solar cells.