Local Speed Research Articles

Precise and accurate speed measurements in rapidly flowing dense suspensions

We introduce a method for precise and accurate measurements of particle speeds in dense suspensions flowing at high rates and demonstrate the utility of the approach for revealing complex flow fluctuations during shearing in a setup that combines imaging with a confocal microscope and shearing with a rheometer. We scan the focal point in one dimension, aligned with direction of flow, producing absolute measurements of speed that are independent of suspension structure and particle shape. We compare this flow-direction line scanning approach with a complementary method we introduced previously, measuring speed using line scanning in the vorticity direction. By comparing results in various flow conditions, including shear-thinning and thickening regimes, we demonstrate the efficacy of our new approach. We find that both approaches exhibit qualitatively similar flow profiles, but a comparative analysis reveals a 15%–25% overestimation in speed measurement using vorticity line scanning, with discrepancies generated by anisotropic suspension microstructure under flow. Moreover, in the thickening regime where complex flow fields are present, both approaches capture local speed fluctuations. However, line scanning in the flow direction reveals and precisely captures stagnation and backflows, a capability not achievable with vorticity line scanning. The approach introduced here not only provides a refined technique for speed measurement in fast-flowing suspensions but also emphasizes the significance of accurate measurement techniques in advancing our understanding of flow behavior in dense suspensions, particularly in contexts where strong non-affine flows are prevalent.

An Investigation into Using CFD for the Estimation of Ship Specific Parameters for the SPICE Model for the Prediction of Sea Spray Icing: Part 2—The Verification of SPICE2 with a Full-Scale Test

A hybrid CFD–ML model for the prediction of sea spray icing, SPICE2, was developed in Part 1 of this study in Deshpande et al., 2024. The SPICE2 model is an extension of the ML model, SPICE, where some of the variables required for icing rate predictions: local wind speed, spray duration, spray period, and spray flux, are computed from CFD simulations. These, along with the air and water temperatures, and the salinity from the metocean data are used for the prediction of icing rates at different locations on a moving vessel. The existing full-scale icing measurements proved to be not detailed enough for the purpose of the verification of sea spray icing prediction models and the verification of the SPICE2 required distribution of sea spray icing data on the vessel surface in addition to the vessel design for simulation. A full-scale sea spray icing test was conducted in 2018 by Sundsbø et al. on a fully enclosed lifeboat equipped for the Goliat field in the Barents Sea. The 3D design of the same lifeboat, together with the corresponding metocean conditions and ship characteristics was used for the simulation of the vessel-specific parameters required for the verification of the icing rate and distribution prediction from the SPICE2 model against the measured distribution of sea spray icing rates on the lifeboat surface. The availability of the 3D model of this lifeboat, in addition to the fact that the icing measurements from this test were detailed enough to attempt a model verification served the purpose of validating the SPICE2 model. The icing rates measured on this lifeboat are used for the full-scale validation of the SPICE2 model that is proposed in Part 1 of this study. It was seen that the icing rates predicted by SPICE2 concurred with 9 of 13 selected locations on the lifeboat. The ones which did not showed very little deviation from the measurements. The icing rate and distribution prediction with SPICE2 were satisfactorily validated against full-scale icing measurements. This is a first attempt in modelling sea spray generation using CFD and further research into CFD for the estimation of spray flux is suggested.

A machine learning approach uncovers principles and determinants of eukaryotic ribosome pausing.

Nonuniform local translation speed dictates diverse protein biogenesis outcomes. To unify known and uncover unknown principles governing eukaryotic elongation rate, we developed a machine learning pipeline to analyze RiboSeq datasets. We find that the chemical nature of the incoming amino acid determines how codon optimality influences elongation rate, with hydrophobic residues more dependent on transfer RNA (tRNA) levels than charged residues. Unexpectedly, we find that wobble interactions exert a widespread effect on elongation pausing, with wobble-mediated decoding being slower than Watson-Crick decoding, irrespective of tRNA levels. Applying our ribosome pausing principles to ribosome collisions reveals that disomes arise upon apposition of fast-decoding and slow-decoding signatures. We conclude that codon choice and tRNA pools are evolutionarily constrained to harmonize elongation rate with cotranslational folding while minimizing wobble pairing and deleterious stalling.

Traveling Wave Amplification in Stationary Gratings.

We show that a grating amplitude stationary in space but oscillating in time can be modeled as independent gratings traveling in opposite directions, interacting almost exclusively with waves traveling in the same direction. We reproduce the key features of traveling gratings: Amplification, field compression, and photon production are evident when the local wave speed equals the grating velocity. We speculate that these stationary but oscillating gratings may prove easier to realize experimentally than traveling gratings.

Array analysis of P-wave polarization data for constraining near-surface shear wave speeds: Application to the valley of Mexico seismic network

Summary We present an array method for constraining near-surface shear wave speed values from P-wave polarization data. As the direction of P-wave particle motion is determined both by the incident ray parameter and the local surficial shear wave speed, the latter can be inferred from three-component seismic records if the ray parameter is estimated first. Previous studies have analyzed data recorded at a single station from various earthquakes in order to estimate near-surface shear wave speed. In contrast, the present article analyzes data recorded at an array of stations. As such, this method can estimate both ray parameter data for the recorded earthquakes and near-surface shear wave speed values at the studied sites. And as an advantage, the method can analyze records of local and regional earthquakes where ray parameter information cannot be reliably estimated using 1-D reference velocity models. Additionally, by processing polarization data spanning multiple events and stations, our method provides robust estimates for near-surface shear wave speed values. We apply this array-based method to a seismic array covering the Valley of Mexico. Analyzed records correspond to regional earthquakes with epicentral distances of roughly 300 km. The corresponding ray parameter values for all earthquakes were estimated from the delay of the seismic waves arrival throughout the network as all stations have a reliable time reference. Near-surface shear wave speed values estimated from the array-based polarization analysis are correlated with major geological features of the area such as the Trans-Mexican Volcanic Belt and the local basin. We also apply the presented method to a minimal data scenario. The flexibility of this method allows it to employ a single ray parameter value as input, producing robust estimates for near-surface shear wave speed values within our case study. This feature becomes useful in a variety of scenarios, such as utilizing seismic data lacking timing information. The presented method constitutes an accessible and inexpensive alternative to study shallow seismic structure, opening opportunities to expand and complement current tools and techniques to assess seismic hazards.

Flame dynamic insights into pollutant production characteristics of NH[formula omitted]/H[formula omitted]/air combustion during turbulent flame–wall interaction

A turbulent premixed NH3/H2/air flame with Damköhler number Da = 0.004 has been simulated using direct numerical simulations (DNS) with detailed chemistry. The flame is propagating in a fully-developed turbulent channel flow with friction Reynolds number Reτ = 313. The global and local production speeds of NO, N2O and NH3 have been analyzed. Special attention is paid on the flame dynamic insights into the pollutant production characteristics during the flame–wall, flame–turbulence and flame–flame interaction process. Finally, important findings have been concluded: (1) The unburned NH3 is reduced in the turbulent case due to the smaller quenching distance; (2) The N2O consumption speed would decrease just before flame quenching on the wall in the turbulent case, due to the thickened flame thickness, which is contradictory to the laminar case; (3) Effect of flame curvature on pollutant production speeds should be combined with the effect of flame surface density; (4) Low Damköhler number flame tends to produce more N2O and less NO, due to the more frequent flame–flame interaction events. These findings would help to better understand the pollutant production characteristics of turbulent premixed NH3/H2/air flames in lab-scale burners with strong flame–wall interactions.Novelty and Significance StatementThe novelty of this research is summarized into 3 points: (1) It is, to our knowledge the first time, pollutant production characteristics are investigated in detail for the premixed NH3/H2/air flame propagating in a fully developed turbulent boundary layer; (2) The effects of flame–wall, flame–turbulence and flame–flame interactions, the underlying reasons and controlling mechanisms are investigated in detail, which has not yet been (comprehensively) done in previous studies; (3) Novel and important findings have been concluded for the wall effects, turbulence effects (including flame curvature and surface density function effects) and flame–flame interaction effects. Some of these findings have never been reported before, but important for the comprehensive understanding of the pollutant production characteristics. It is significant because understanding the turbulence and wall effects on NO, N2O and NH3 production characteristics in the premixed NH3/H2/air flames is crucial for the future clean combustion energy system design.

Improving the wind map using interpolation approach: Case study of a southeastern region of Algeria

Various software programs like Wind Atlas Analysis and Application Program (WAsP) and WindSim use only a single data source, limiting the information generated to a small area, while it is more interesting to look for the contribution that can be added to the local speed generated at a certain distance. To improve accuracy of the prediction, refining wind maps with data from neighboring masts and using new interpolation methods is proposed in this paper. Thus, to make corrections to the local speed generated at a certain distance, we propose to refine the wind maps produced by numerical simulations of WAsP software, taking into account the data from neighboring masts and optimizing the results by new interpolation methods. This technique is applied to a region of southeastern Algeria, where the wind data were collected from three meteorological stations, namely of Biskra, Guemar, Sidi Mahdi, and three neighboring stations of Tunisia namely Gafsa, Kebili and Nefta. The results shown that depending on the characteristics and the surrounding environment of the measuring mast, the simulated wind speed can vary between 5% and 25%.

To Study the Construction Techniques for Slum Rehabilitation In Metro Cites

With increasing urbanization, the need for adequate affordable shelter also increases. Housing shortage is one of the major challenges faced in developing countries like India. It is imperative to provide housing that caters to all income groups with special focus on the low and middle income groups. In order to provide low cost housing, it is essential to adopt innovative sustainable technologies which are affordable, fast track, cost effective, durable as well as environment friendly. This seminar discusses emerging innovative technologies that can be adopted in the country to lower housing construction cost and help in provision of mass affordable housing. Some of the technologies discussed are Tunnel form, Sismo building technology, Light Gauge Steel Framed Structural technology, Precast Sandwich Panel technology etc. These technologies are reviewed on various parameters like local availability, affordability, energy efficiency, structural stability, quality, speed, cost effectiveness, eco friendliness and suitability for high rise construction. The advantages and limitations of the technologies are also mentioned. The seminar highlights the potential barriers for adoption of these technologies in the Indian urban scenario and methods which can be employed to overcome those barriers in order to provide affordable housing for the low and middle income groups. Fast track construction is very much essential for the mass housing projects like slum rehabilitation . For this rapid wall technique can be best option for construction

Brief communication: A simple axial induction modification to the Weather Research and Forecasting Fitch wind farm parameterization

Abstract. We propose a modification to the Fitch wind farm parameterization implemented in the Weather Research and Forecasting (WRF) model. This modification, derived from 1D momentum theory, employs a wind-speed-dependent induction factor to correct the local grid wind speed back to freestream before computing the turbine's power and thrust. While the original implementation underestimates power, the modified version shows good agreement with the power curve. We strongly recommend employing the modification for all studies that model at maximum one turbine per WRF grid cell. For simulations with more turbines per grid cell, additional inner-cell wake losses have to be considered.

The local ship speed reduction effect on black carbon emissions measured at a remote marine station

The local ship speed reduction effect on black carbon emissions measured at a remote marine station

Sliding into DM: determining the local dark matter density and speed distribution using only the local circular speed of the galaxy

We use FIRE-2 zoom simulations of Milky Way size disk galaxies to derive easy-to-use relationships between the observed circular speed of the Galaxy at the Solar location, v c, and dark matter properties of relevance for direct detection experiments: the dark matter density, the dark matter velocity dispersion, and the speed distribution of dark matter particles near the Solar location. We find that both the local dark matter density and 3D velocity dispersion follow tight power laws with v c. Using this relation together with the observed circular speed of the Milky Way at the Solar radius, we infer the local dark matter density and velocity dispersion near the Sun to be ρ = 0.42±0.06 GeV cm-3 and σ 3D = 280+19 -18 km s-1. We also find that the distribution of dark matter particle speeds is well-described by a modified Maxwellian with two shape parameters, both of which correlate with the observed v c. We use that modified Maxwellian to predict the speed distribution of dark matter near the Sun and find that it peaks at a most probable speed of 257 km s-1 and begins to truncate sharply above 470 km s-1. This peak speed is somewhat higher than expected from the standard halo model, and the truncation occurs well below the formal escape speed to infinity, with fewer very-high-speed particles than assumed in the standard halo model.

The vacuum-core vortex in relativistic perfect fluids

The governing equations in non-relativistic (conventional) compressible fluid flows are derived as a low-energy limit in relativistic flows. This suggests that exact solutions obtained in non-relativistic fluid dynamics possess their relativistic counterparts. As an example of such solutions, we consider a stationary potential flow and examine the relativistic effect on a vacuum-core (hollow-core) vortex solution in compressible fluid flows. The vacuum-core vortex solution is an exact solution in stationary potential flows, which is also true in relativistic flows. We construct the vacuum-core vortex solution in relativistic fluid flows and discuss the differences and similarities between non-relativistic and relativistic flows. We show that the vacuum-core radius in relativistic flows is larger than the one in non-relativistic flows for a fixed polytropic exponent and depends on the transonic speed (local sound speed) in the flow field. We also calculate various physical quantities such as density, pressure, and sound speed as functions of radius r from the center of the core and compare them with those in non-relativistic flows.

A Structure-Preserving Semi-implicit IMEX Finite Volume Scheme for Ideal Magnetohydrodynamics at all Mach and Alfvén Numbers

We present a divergence-free semi-implicit finite volume scheme for the simulation of the ideal magnetohydrodynamics (MHD) equations which is stable for large time steps controlled by the local transport speed at all Mach and Alfvén numbers. An operator splitting technique allows to treat the convective terms explicitly while the hydrodynamic pressure and the magnetic field contributions are integrated implicitly, yielding two decoupled linear implicit systems. The linearity of the implicit part is achieved by means of a semi-implicit time linearization. This structure is favorable as second-order accuracy in time can be achieved relying on the class of semi-implicit IMplicit–EXplicit Runge–Kutta (IMEX-RK) methods. In space, implicit cell-centered finite difference operators are designed to discretely preserve the divergence-free property of the magnetic field on three-dimensional Cartesian meshes. The new scheme is also particularly well suited for low Mach number flows and for the incompressible limit of the MHD equations, since no explicit numerical dissipation is added to the implicit contribution and the time step is scale independent. Likewise, highly magnetized flows can benefit from the implicit treatment of the magnetic fluxes, hence improving the computational efficiency of the novel method. The convective terms undergo a shock-capturing second order finite volume discretization to guarantee the effectiveness of the proposed method even for high Mach number flows. The new scheme is benchmarked against a series of test cases for the ideal MHD equations addressing different acoustic and Alfvén Mach number regimes where the performance and the stability of the new scheme is assessed.

Propagation Characteristics of Initial Compression Wave Induced by 400 km/h High-Speed Trains Passing through Very Long Tunnels

When high-speed trains enter tunnels, an initial compression wave is generated. As the compression wave propagates at the local speed of sound to the tunnel exit, it radiates into the surrounding environment, forming micro-pressure waves (MPWs). MPWs create sonic booms, resulting in significant environmental issues. The magnitude of the micro-pressure waves is directly proportional to the pressure gradient of the compression wave at the tunnel exit. The nonlinear effects of the initial compression wave during propagation lead to a significant increase in pressure gradient. Therefore, the propagation characteristics of the initial compression wave during the tunnel are the crucial factor affecting the amplitude of MPWs. Based on the one-dimensional compressible unsteady non-isentropic flow model and the improved generalized Riemann variable characteristic method, this paper researched the propagation and evolution characteristics of an initial compression wave generated when 400 km/h high-speed trains enter tunnels with three portal shapes: (no tunnel entrance hood (no hood), an oblique, enlarged tunnel entrance hood (type A), an enlarged equal-section non-uniform opening hole tunnel entrance hood (type B)). The results show that when the initial compression wave propagates inside very long tunnels, the pressure gradient of the compression wave exhibits a trend of initially increasing and then decreasing with the increase in propagation distance. When the pressure gradient of the compression wave reaches its maximum value, the corresponding propagation distance is the steepening critical distance. For no tunnel entrance hoods, type A tunnel entrance hoods, and type B tunnel entrance hoods, the steepening critical distances are 5 km, 6 km, and 16 km, respectively. The steepening critical distance shortens with increasing train speed. Steady friction and unsteady friction effects mainly affect the pressure amplitude and pressure gradient during compression wave propagation, respectively. At lower ambient temperatures, the nonlinear effects in compression wave propagation are significantly enhanced. The mitigation effects of type A tunnel entrance hoods and type B tunnel entrance hoods on pressure gradient reduction are mainly concentrated within 4 km and 12 km, respectively. It is necessary to determine the optimal matching relationship between the tunnel entrance hood and tunnel length based on the characteristics of compression wave propagation to ensure their mitigating performance is maximized.

A block-spectral adaptive H-/p-refinement strategy for shock-dominated problems

An adaptive H-/p-refinement strategy using a novel sensor is devised and tested in a block-spectral compressible Euler code equipped with adaptive-mesh refinement (AMR) and high-order flux-reconstruction numerics. At each Gauss quadrature point (or solution point) within each spectral block (or mesh element) the discrete velocity jump ΔU = ∂U/∂y1Δy1+∂V/∂y2Δy2+∂W/∂y3Δy3 is calculated and normalized by the local speed of sound, a. The grid spacing, Δxi, is calculated in each direction as the distance between auxiliary Gauss-Lobatto points, staggered relative to the solution points. The polynomial order is increased from p=0 to p=pmax in regions of weak compression, (ΔU/a)crit<ΔU/a<0 and kept at p=pmax in regions of flow expansion ΔU/a≥0, while staying at the H=0 base mesh level. Regions experiencing strong compressions, i.e. ΔU/a<(ΔU/a)crit, are H-refined up to H=Hmax where Hmax is applied at the location of maximum compression, ΔU/a=min(ΔU/a) in the domain, while keeping p=0 to guarantee robustness and monotonicity of the solution in the H refined region. The critical value of (ΔU/a)crit= -0.06 is found to effectively separate smooth and non-smooth solution regions, supported by a 1D detonation initiation test case in ideal gas and a shock-to-detonation transition in high explosives. Using this value, the Sod shock tube, Shu-Osher problem, double Mach reflection and a 2D detonation in a high-explosive are simulated with the proposed adaptive H-/p-refinement. In the Sod shock tube case, p-refinement resolves the (weak) contact discontinuity while H-refinement enhances the grid resolution in the shock exploiting the monotonicity of the p=0 reconstruction. For the Shu-Osher problem, p-refinement captures the small-scale oscillations trailing the shock that would be otherwise attenuated, while H-refinement triggered by the ΔU-sensor appropriately tracks the shock. In the double Mach reflection problem, H-refinement confines the numerical diffusion around the reflected shock while p-refinement recaptures many physical features trailing the shock. Finally, in the 2D high-explosive detonation case, H-refinement follows the leading shock and resolves the curvature of the detonation wave, while p-refinement adds resolution to the trailing reaction zone. Finally, the proposed methodology is tested in a detonation-wave propagation test case in high-explosives with numerical predictions comparing favorably against experiments.

Design and research of heat dissipation system of electric vehicle lithium-ion battery pack based on artificial intelligence optimization algorithm

This research focuses on the design of heat dissipation system for lithium-ion battery packs of electric vehicles, and adopts artificial intelligence optimization algorithm to improve the heat dissipation efficiency of the system. By integrating genetic algorithms and particle swarm optimization, the research goal is to optimize key design parameters of the cooling system to improve temperature control and extend battery life. In the process of algorithm implementation, genetic algorithm improves the diversity of population through crossover and mutation operations, thus enhancing the global search ability. Particle swarm optimization (PSO) improves local search accuracy and convergence speed by dynamically adjusting inertia weight and learning factor. The effects of different design schemes on heat dissipation performance were systematically evaluated by using computational fluid dynamics (CFD) software. The experimental results show that the efficiency of the cooling system is significantly improved after the application of the optimization algorithm, especially in the aspects of temperature distribution uniformity and maximum temperature reduction. The optimization algorithm also successfully shortens the thermal response time of the system and improves the adaptability and stability of the system under different working conditions. The computational complexity and execution time of these algorithms are also analyzed, which proves the efficiency and feasibility of these algorithms in practical applications. This study demonstrates the practicability and effectiveness of artificial intelligence optimization algorithm in the design of heat dissipation system of lithium-ion battery pack for electric vehicles, and provides valuable reference and practical guidance for the progress of heat dissipation technology of electric vehicles in the future.

Aerodynamic analysis of vertical axis wind turbines at various turbulent levels: Insights from 3D LES simulations

This study provides novel insights into the aerodynamic performance of vertical axis wind turbines (VAWTs) in diverse turbulent environments using 3D large eddy simulations (LES). The research aims to understand how turbulence affects VAWT aerodynamics across different operational conditions, including varying wind speeds, tip speed ratios (TSRs), and turbulence intensities. Validation against wind tunnel test data from literature enhances the reliability of the findings. The study utilizes the consistent discrete random flow generation (CDRFG) technique to create homogeneous turbulence in the approaching flow. Through a detailed examination of the rotor aerodynamics and flow fields, critical insights are gained into the impact of turbulence on VAWT performance. The results highlight that local wind speed fluctuations influence the onset of dynamic stall, with turbulence significantly affecting lift forces, while having negligible effects on drag. Notably, fluctuations in the one-cycle power coefficient CP,sgl are primarily attributed to variations in lift. Furthermore, under turbulent conditions, substantial improvements in power performance are observed as wind speed increases from 5 m/s to 10 m/s, with minimal further enhancements at higher wind speeds. These findings contribute valuable insights for optimizing turbine design and operation, particularly in regions characterized by varying wind speeds and turbulence.

Typhoon-induced failure analysis of electricity transmission tower-line system incorporating microtopography

Safety of electricity transmission tower-line systems is always at risk from various natural hazards, especially typhoons, which have the potential to cause significant damage and disruption. However, previous studies have predominantly focused on typhoons occurring in open-flat terrains, disregarding the impact of actual terrains. To address this research gap, this paper comprehensively examines the typhoon-induced responses of an electricity transmission tower-line system while considering the influence of microtopography. Specifically, a virtual wind tunnel is first established to simulate the typhoon within a specific microtopography. The wind characteristics of the typhoon considering the influence of microtopography are then studied in terms of the average wind speed, gust factor, and turbulence intensity, and compared to those at open-flat terrains. Finally, the influences of microtopography on the typhoon-induced responses and collapse of the electricity transmission tower-line system are investigated. The results demonstrate that the introduction of microtopography can obviously increase local wind speed and enhance fluctuation of wind, resulting in large responses and collapse risk of electricity transmission tower-line systems. This paper could provide a valuable reference for assessing the wind-resistant capacity of electricity transmission lines located in complex terrains, e.g., mountains and valleys.

A Kepler optimization algorithm improved using a novel Lévy-Normal mechanism for optimal parameters selection of proton exchange membrane fuel cells: A comparative study

Proton exchange membrane fuel cells (PEMFCs) are considered a promising renewable energy source and have sparked a lot of interest over the last few years due to their robust efficiency, low operating temperature, and longevity. The PEMFC's electrochemical model has seven unknown parameters, which are not given in the manufacturer's datasheets and need to be accurately estimated to present a more accurate model, leading to improved efficiency and performance of the PEMFC systems. The estimation of those unknown parameters has been dealt with as a complex and non-linear optimization problem that needs a powerful optimization algorithm to solve it. The existing optimization algorithms still have some disadvantages, such as falling into local minima and low convergence speed, which make them ineligible to solve this complicated problem with acceptable accuracy and low computational cost. Therefore, this study presents a new parameter estimation technique for estimating the unknown parameters of the PEMFC model more accurately, thereby achieving precise modeling of PEMFCs. This technique called IKOA is based on integrating the Kepler optimization algorithm (KOA) with a novel Lévy-Normal (LN) mechanism to strengthen its exploration and exploitation capabilities against this multimodal optimization problem. The Lévy flight in this mechanism aims to improve the KOA's exploitation operator to accelerate the convergence speed toward the near-optimal solution, thus minimizing the computational cost; meanwhile, the normal distribution is used to strengthen its exploration operator, thereby aiding in the escape of local minima. The proposed IKOA and KOA are herein evaluated against several rival algorithms using six well-known commercial PEMFC stacks to highlight their efficiency and effectiveness. Key performance metrics such as computational cost, fitness measures, and statistical validation through the Wilcoxon rank-sum test are herein used to highlight IKOA's effective performance in enhancing the predictive accuracy and operational efficiency of PEMFCs. The numerical findings show the high superiority of IKOA against all rival optimizers on all solved benchmarks.

MATRICS: The implicit matrix-free Eulerian hydrodynamics solver

Context. There exists a zoo of different astrophysical fluid dynamics solvers, most of which are based on an explicit formulation and hence stability-limited to small time steps dictated by the Courant number expressing the local speed of sound. With this limitation, the modeling of low-Mach-number flows requires small time steps that introduce significant numerical diffusion, and a large amount of computational resources are needed. On the other hand, implicit methods are often developed to exclusively model the fully incompressible or 1D case since they require the construction and solution of one or more large (non)linear systems per time step, for which direct matrix inversion procedures become unacceptably slow in two or more dimensions. Aims. In this work, we present a globally implicit 3D axisymmetric Eulerian solver for the compressible Navier–Stokes equations including the energy equation using conservative formulation and a fully simultaneous approach. We use the second-order-in-time backward differentiation formula for temporal discretization as well as the κ scheme for spatial discretization. We implement different limiter functions to prohibit the occurrence of spurious oscillations in the vicinity of discontinuities. Our method resembles the well-known monotone upwind scheme for conservation laws (MUSCL). We briefly present efficient solution methods for the arising sparse and nonlinear system of equations. Methods. To deal with the nonlinearity of the Navier–Stokes equations we used a Newton iteration procedure in which the required Jacobian matrix-vector product was reconstructed with a first-order finite difference approximation to machine precision in a matrix-free way. The resulting linear system was solved either completely matrix-free with a combination of a sufficient Krylov solver and an approximate Jacobian preconditioner or semi-matrix-free with the incomplete lower upper factorization technique as a preconditioner. The latter was dependent on a standalone approximation of the Jacobian matrix, which was optionally calculated and needed solely for the purpose of preconditioning. Results. We show our method to be capable of damping sound waves and resolving shocks even at Courant numbers larger than one. Furthermore, we prove the method’s ability to solve boundary value problems like the cylindrical Taylor-Couette flow (TC), including viscosity, and to model transition flows. To show the latter, we recover predicted growth rates for the vertical shear instability, while choosing a time step orders of magnitude larger than the explicit one. Finally, we verify that our method is second order in space by simulating a simplistic, stationary solar wind.