The LOLland offshore Lidar EXperiment, or LOLLEX, ran from September 2022 to August 2023 in and around the Rødsand II wind farm, a 90-turbine array in the shallow waters of the Baltic Sea just south of the island of Lolland. Led by Shokoufeh Malekmohammadi of the University of Bergen, with colleagues from Bergen, the Technical University of Denmark and RWE, the campaign was part of the EU-funded Train2Wind training network. Its central innovation was deceptively simple: instead of bolting instruments to fixed platforms or buoys, the team mounted two commercial Doppler wind lidars on a crew transfer vessel that commuted nearly every day between Rødby harbour and the wind farm, turning routine maintenance traffic into a mobile atmospheric observatory.

The instrumentation combined complementary strengths. A WindCubeV2 lidar wind profiler measured wind speed and direction at eleven heights from 40 to 290 metres above the sea, sampling at 0.25 hertz and relying on an integrated inertial measurement unit and satellite positioning to track the vessel’s every pitch, roll and heave. Alongside it, a WindCube100S scanning lidar alternated on a 30-minute cycle between five-beam wind profiling and a vertical staring mode that sampled the vertical wind velocity at 1 hertz with 10-metre along-beam resolution, reaching as high as about 2.5 kilometres. Later in the campaign, a long-range Halo Photonics StreamLine XR+ scanning lidar was installed on the wind farm’s transformer platform to the north, firing range-height-indicator scans across the array every 16 seconds to capture turbine wakes in a fixed vertical plane.

Measuring wind from a moving ship is far harder than it sounds. Vessel motion corrupts the lidar’s line-of-sight velocity with spurious contributions from surge, sway, heave, roll, pitch and yaw, and while 10-minute averages on buoys are only marginally affected, instantaneous turbulence measurements can be badly distorted. The team applied a two-step motion correction that projects the platform’s translational velocity onto each beam and then uses time-resolved attitude data to build a rotation matrix, retrieving the true wind vector by least squares. The payoff was substantial: for the WindCubeV2, correction improved the coefficient of determination against the NORA3 reanalysis from 0.797 to 0.892 and cut the root-mean-square error from 1.9 to 1.5 metres per second, bringing ship-based performance close to that of fixed lidars.

The scanning lidar posed a subtler problem. In vertical stare mode only one equation is available for three unknown wind components, so dynamic motion correction is impossible. Instead, the researchers developed a static tilt correction, estimating that the instrument sat at a mean inclination of about 2.7 degrees and removing the resulting bias in the vertical velocity. Without correction the scanning lidar showed a median vertical velocity bias of 0.28 metres per second; a naive zero-mean assumption over-corrected to minus 0.20, while the new tilt method reduced the bias to just minus 0.05 metres per second, the smallest error and the tightest distribution of the three approaches.

With the corrections validated, the dataset, several thousand hours of vertical scans and more than 2,100 hours of platform-based RHI data, became a window into processes rarely seen above the open sea. The most striking case study captured Kelvin-Helmholtz billows on 22 February 2023, wave-like instabilities that form when vertical wind shear across a stable atmospheric layer exceeds a critical threshold. Between 550 and 750 metres altitude, the lidar recorded billows that grew, overturned and dissipated over roughly eleven minutes, their passage visibly broadening and lowering a layer of accumulated aerosol beneath the capping inversion. The accompanying surge in vertical velocity variance within a 400-metre-thick slab suggests exactly the kind of enhanced downward mixing that could, if billows overlap with turbine wakes, accelerate wake recovery under stable conditions.

A second case the same morning revealed a different flavour of entrainment. Around sunrise, with the vessel still in harbour, turbulence intensified near 600 metres and spread downward through a 200-to-800-metre layer, consistent with buoyancy-driven mixing initiated at the top of the boundary layer, possibly by cloud-top radiative cooling. Unlike the textbook Kelvin-Helmholtz event, this episode lacked narrow-band spectral peaks and organised periodic structures, pointing instead to intermittent, vertically distributed entrainment. That evening, a third case study detected internal atmospheric waves with a two-minute period riding near the nose of a 15-metre-per-second low-level jet, shear-driven oscillations spanning roughly 200 metres in height that may amplify mixing and wake meandering when they occur above wind farms.

The fourth case study turned to wakes themselves. During easterly flow on 13 June 2023, the platform-mounted lidar’s RHI scans resolved multiple turbine wakes stretching across the farm, which the team compared against virtual scans generated with the open-source PyWake framework driven by NORA3 inflow data. Most analytical wake deficit models, including the classic Jensen and several Gaussian variants, underestimated the observed wake losses, predicting wind speeds higher than those measured. Only the TurboGaussian model, closely related to Ørsted’s TurbOPark concept, produced longer wakes and larger velocity deficits in good agreement with the lidar, echoing earlier findings that common models overestimate wake recovery between Rødsand II and the neighbouring Nysted farm.

The campaign’s limitations are as instructive as its successes. Safety rules kept the vessel ashore when significant wave heights exceeded two metres or winds surpassed 12 metres per second, and in-farm observations were confined to daylight working hours. The mechanically steered WindCube100S proved sensitive to vessel translation, with 65 percent of its vertical stare scans flagged incomplete, while the optically switched WindCubeV2 maintained roughly 3.5 times higher data availability. Missing temperature and humidity profiles prevented a definitive separation of buoyancy- and shear-driven processes. Even so, the researchers argue that crew transfer vessels offer a cost-effective, mobile platform for shallow coastal seas, and that future deployments with gyroscopic stabilisation and co-located thermodynamic profiling could extend the approach to harsher environments, turning the everyday commute of offshore service boats into a routine probe of the atmosphere that governs how much power the world’s wind farms actually deliver.

Subject of Research: Ship-based Doppler lidar observations of wind farm flow, wake recovery and vertical momentum entrainment in the marine atmospheric boundary layer

Article Title: The LOLland offshore Lidar EXperiment (LOLLEX): a novel observational approach for the study of wind farm flow and entrainment

Article References: Malekmohammadi, S., Cheynet, E., Reuder, J., Linnemann, C., Sjöholm, M., Mann, J., & Giebel, G. (2026). The LOLland offshore Lidar EXperiment (LOLLEX): a novel observational approach for the study of wind farm flow and entrainment. Atmospheric Measurement Techniques, 19(19), 6267-6292. https://doi.org/10.5194/amt-19-6267-2026

Image Credits: AI Generated

DOI: 10.5194/amt-19-6267-2026

Keywords: offshore wind energy, Doppler wind lidar, atmospheric boundary layer, wake recovery, momentum entrainment, Kelvin-Helmholtz instability, turbulence, Rødsand II wind farm, crew transfer vessel, remote sensing, wind profiler, atmospheric waves

The mathematical heart of the work lies in a careful treatment of the linearized equations of ideal, incompressible magnetohydrodynamics in a 2.5-dimensional configuration, meaning that the equilibrium quantities vary in only one direction while the perturbations are allowed to depend on all three spatial coordinates. Hosseinpour considers a shear layer in which a plasma flow slides past a magnetized background, the classic setup for the Kelvin-Helmholtz instability. The key insight is that the operator governing the instability in this magnetized system is neither self-adjoint nor normal. In plain terms, the eigenmodes that describe the growing ripples do not behave like the well-behaved orthogonal modes of a simple vibrating string. Instead, they carry a complex phase asymmetry across the shear layer, with different parts of the wave pattern shifted in phase relative to one another in a way that a symmetric, self-adjoint operator would never produce.

That phase asymmetry turns out to have a striking physical consequence. When the perturbed electric field and the perturbed vector potential of the reference potential field are combined in the flux integral that governs relative magnetic helicity, the time average over an oscillation cycle no longer cancels to zero. The growing instability therefore drives a nonzero, time-averaged helicity flux through the boundaries of any subvolume that encloses the shear layer. Because the system is closed, this flux does not create helicity from nothing in a global sense; rather, it redistributes magnetic helicity within the system, pumping it from the shear interface into the surrounding jet boundary region. The paper derives a closed-form analytical expression for this helicity injection rate, a result that required a meticulous gauge-invariant formulation of the relative helicity evolution equation.

Gauge invariance is not a technical nicety here but the foundation of the claim. Magnetic helicity is defined through integrals involving the vector potential, and the vector potential is only defined up to a gauge transformation, an arbitrary redefinition that leaves the physical magnetic field unchanged. A sloppy gauge choice can make a helicity flux appear or disappear artificially. The study works through the derivation in detail, choosing the gauge on the boundaries of the slab so that the vector potential of the perturbed field matches that of the potential reference field in the components tangential to the boundary. With that choice, the volume terms in the helicity evolution equation vanish identically in ideal magnetohydrodynamics, because the electric field is perpendicular to the magnetic field, and the entire budget reduces to a surface integral of the cross product between the perturbed electric field and the vector potential of the reference field. The surviving expression is unambiguously gauge-invariant, which means the helicity injection it predicts is a genuine physical effect rather than an artifact of the bookkeeping.

The mathematical machinery behind the result is equally careful. Starting from the linearized momentum and induction equations, the analysis eliminates the total pressure perturbation and reduces the full system to a single second-order ordinary differential equation for the velocity perturbation normal to the shear layer. For the specific equilibrium considered, in which the flow profile is proportional to the magnetic field profile, a clever change of variables collapses the equation into a compact form that can be solved with a matched asymptotic expansion. In the outer regions far from the shear layer, where the equilibrium quantities are effectively constant, the equation reduces to a simple Helmholtz equation. Matching the inner and outer solutions then yields the eigenfunctions whose complex phase structure, as the paper shows, is precisely what generates the helicity flux.

To test whether the effect matters in the real universe, Hosseinpour applies the theory to solar coronal jets, narrow, spire-shaped eruptions that shoot plasma along magnetic field lines out of the solar corona. Observations frequently show Kelvin-Helmholtz instability developing at the interface between the fast-moving jet and the slower ambient plasma, appearing as rolled-up vortices along the jet boundary. Using representative jet parameters, a background magnetic field of roughly 10 gauss, a shear-flow Alfvén Mach number of about 1.36, a shear layer half-width of approximately 100 kilometers, and a perturbation wavelength near 10,000 kilometers, the calculation predicts that the linear instability injects magnetic helicity at a rate of about 10 to the 14 webers squared per second into the jet boundary region.

That number becomes meaningful when integrated over the growth time of the instability. The e-folding time, the interval over which the perturbation amplitude grows by a factor of e, is estimated at roughly 8 seconds for these parameters. Over such an interval, the accumulated helicity reaches a magnitude comparable to the twist inferred from observations of active-region jets. This is the most provocative claim of the paper: the Kelvin-Helmholtz instability alone may provide enough magnetic helicity to account for the helical structure and the resulting stability of coronal jets. If correct, it would resolve a persistent puzzle in solar physics, namely where the twist in these structures comes from, and it would elevate the instability from a mere source of turbulence to an active agent in structuring the corona.

The broader implications extend well beyond the Sun. Kelvin-Helmholtz instability is ubiquitous wherever fast plasma flows shear past slower ones, from the flanks of Earth’s magnetosphere, where rolled-up vortices have been observed to transport solar wind into the magnetosphere, to coronal streamers, prominence bubbles, and the boundaries of astrophysical jets on far larger scales. Magnetic helicity conservation and transport are central to dynamo theory, to the build-up of coronal mass ejections, and to the self-organization of laboratory plasmas. A mechanism that generates helicity flux at any sheared magnetized interface therefore touches a remarkably wide swath of plasma physics, and the analytical nature of the result makes it a benchmark against which numerical simulations can be tested.

It is worth emphasizing the limits of the theory as presented. The derivation assumes an ideal, incompressible plasma in a 2.5-dimensional geometry, with a linear analysis valid only while the perturbations remain small. Real coronal plasmas are compressible, the instability eventually saturates and becomes turbulent, and the nonlinear phase, where vortices merge and magnetic reconnection may set in, lies beyond the reach of the linear eigenmode calculation. The author notes that no datasets were generated or analyzed in the study, underscoring that this is a purely theoretical contribution whose predictions now await observational or numerical scrutiny. Still, the elegance of the result is hard to deny: a closed-form, gauge-invariant expression showing that the very act of shearing a magnetized flow imprints twist on the magnetic field. If future observations confirm that coronal jets acquire their helical structure on the timescales predicted, textbooks may need to add a new entry to the list of ways the universe winds up its magnetic fields, and it will be one written by an instability that scientists have been studying for more than a century and a half.

Subject of Research: Generation of magnetic helicity by the Kelvin-Helmholtz instability in magnetized plasma and its application to solar coronal jets

Article Title: On the generation of magnetic helicity by the Kelvin-Helmholtz instability in 2.5D incompressible MHD

Article References: Hosseinpour, M. (2026). On the generation of magnetic helicity by the Kelvin-Helmholtz instability in 2.5D incompressible MHD. Astrophysics and Space Science, 371(10), Article 115. https://doi.org/10.1007/s10509-026-04648-3

Image Credits: AI Generated

DOI: 10.1007/s10509-026-04648-3

Keywords: magnetic helicity, Kelvin-Helmholtz instability, magnetohydrodynamics, solar corona, coronal jets, plasma physics, shear flow, MHD instabilities, solar physics, gauge invariance, magnetic flux ropes, space plasma

The problem begins with the sheer range of scales involved. In the final orbit before two neutron stars merge, the stellar cores slam into one another at a substantial fraction of the speed of light, generating a shear layer roughly a kilometer wide that becomes Kelvin-Helmholtz unstable. This instability, the same mechanism that shapes wind-blown clouds on Earth, shreds the interface between the stars into vortices that fragment into ever smaller eddies, producing a turbulent cascade that spans from kilometer scales down to about a nanometer, where viscosity finally converts kinetic energy into heat. The Reynolds number of this flow, a measure of the ratio of inertial to viscous forces, is a staggering ten to the power of sixteen. Simulating every eddy directly, the approach known as direct numerical simulation, would require computational resources that scale as the Reynolds number cubed, making it utterly impossible for the foreseeable future.

Turbulence is not confined to the moment of contact. Once the stars have merged, the remnant, whether a massive neutron star or a newly formed black hole, is typically encircled by a hot, dense accretion disk. There, the magnetorotational instability stirs the plasma, redistributing angular momentum and governing how matter spirals inward or is flung outward. The way turbulence transports angular momentum determines whether the remnant neutron star collapses promptly to a black hole or survives as a long-lived object, and it controls the mass ejection that powers the kilonova flashes and the nucleosynthesis of r-process elements. It may also amplify magnetic fields to magnetar levels, potentially launching the relativistic jets that produce short gamma-ray bursts. In short, nearly every observable signature of a neutron star merger is touched by turbulence somewhere along the way.

The mathematical machinery for handling unresolved turbulence has a long history in Newtonian fluid dynamics. The classic approach, Reynolds averaging, splits the flow into a mean component and fluctuations, yielding equations for the mean motion that contain an extra term, the Reynolds stress, which encapsulates the momentum carried by the unresolved eddies. A more practical alternative for simulations is large-eddy simulation, or LES, in which the equations are filtered over a length scale comparable to the numerical grid. The filtered equations resemble the original ones but include subgrid-scale stresses that must be modeled. The central difficulty, known as the closure problem, is that these stresses depend on information about the small scales that the simulation does not compute, so modelers must supply approximate relations, or closures, that capture the net effect of the missing physics using only the resolved quantities.

Extending this framework to general relativity introduces subtleties that have no Newtonian counterpart. Radice and Hawke review how averaging or filtering the equations of relativistic hydrodynamics produces effective stresses even when the underlying fluid is ideal, and how the nonlinear structure of the fluxes demands additional closure relations, including one for turbulent mass diffusion. More troubling still is the question of covariance: the averaging operations used in practice are tied to a particular slicing of spacetime, which breaks the four-dimensional symmetry of Einstein’s theory. Recent work has explored building the averaging procedure around a physical observer rather than a coordinate slice, showing that the coarse-grained equations then take the form of a non-ideal relativistic fluid, complete with bulk viscosity, shear stresses, and heat transport terms that arise purely from the turbulence. Even the equation of state, the relation linking pressure, density, and energy, is modified by averaging, since fluctuations in density generate corrections that behave like an additional pressure.

In practice, most published neutron star merger simulations to date have used the simplest possible strategy: implicit large-eddy simulation, which sets the subgrid stresses to zero and relies on the intrinsic numerical dissipation of shock-capturing schemes to mimic the effect of unresolved turbulence. This approach has been remarkably successful in other fields, but the review is blunt about its limitations in this context. The modified equation analysis shows that numerical dissipation can indeed act like an effective viscosity, but implicit methods require a significant fraction of the inertial range to be resolved before results converge, and no neutron star merger simulation has yet been demonstrated to be in that regime. The alternative is explicit modeling. Radice’s own general-relativistic large-eddy simulations employ a relativistic version of the Smagorinsky closure, in which the turbulent viscosity is estimated from a mixing length set by the local scale of the flow and the speed of sound. A third family of methods, gradient or approximate-deconvolution models, reconstructs the effect of the filter algebraically and has the advantage of introducing no tunable parameters beyond the filter width itself.

The payoff of these techniques is already visible in the study of magnetic field amplification. Early Newtonian simulations suggested that the Kelvin-Helmholtz instability could amplify even weak seed fields to magnetar strengths of around ten to the fifteenth gauss, but general-relativistic calculations initially failed to reproduce this, simply because their grids were too coarse. Later, extraordinarily high-resolution simulations by Kenta Kiuchi and collaborators showed that the saturated field strength kept climbing with resolution, with no sign of convergence, precisely because the magnetic back-reaction only halts the cascade at centimeter scales, far below anything a global simulation can resolve. When subgrid models were introduced, the picture changed dramatically. Gradient-model simulations by Ricard Aguilera-Miret, Carlos Palenzuela, and colleagues achieved converged results, confirming that weak fields are indeed amplified to ten to the sixteenth gauss and that the statistical properties of the resulting turbulence are remarkably insensitive to the unknown initial magnetic configuration inside the stars, a reassuring result for predictive modeling.

The same simulations revealed tantalizing evidence of an inverse cascade, in which the characteristic scale of the magnetic field grows from roughly half a kilometer immediately after merger to several kilometers a hundred milliseconds later, as turbulent resistivity rearranges field lines into larger structures. Meanwhile, measurements of the effective viscosity generated by magnetic stresses suggest it remains modest in the dense core of the remnant, implying that turbulence is unlikely to distort the post-merger gravitational-wave signal enough to compromise plans to probe the equation of state of nuclear matter with next-generation detectors such as the Einstein Telescope and Cosmic Explorer. On the other hand, turbulence and dynamo action are expected to leave a significant imprint on the long-term evolution of the remnant, its mass ejection, and its multi-messenger emission, from kilonova light curves to the engines of short gamma-ray bursts.

Much remains to be done. The review highlights open questions about whether angular momentum transport accelerates or delays the collapse of the remnant neutron star, about the topology of the amplified magnetic fields and whether tangled configurations better explain the energetics of gamma-ray bursts, and about the formidable challenge of uncertainty quantification in a parameter space already crowded with uncertain inputs. Validation is particularly thorny: unlike wind tunnels, neutron stars offer no laboratory tests, so models must be calibrated against resolved simulations whose own fidelity is uncertain, and tuned to observables, such as gravitational waves and neutrino signals, that differ from the statistical quantities conventionally used in closure validation. The authors anticipate rapid progress on three fronts: simulations that combine sophisticated microphysics, magnetohydrodynamics, and large-eddy closures; improved phenomenological subgrid models tested in local calculations; and data-driven, machine-learned closures that learn the missing physics directly from high-resolution data. As gravitational-wave astronomy enters its next generation, taming turbulence may prove the key to turning collisions of dead stars into precision measurements of matter at its densest.

Subject of Research: Turbulence modelling in general-relativistic simulations of binary neutron star mergers

Article Title: Turbulence modelling in neutron star merger simulations

Article References: Radice, D., & Hawke, I. (2024). Turbulence modelling in neutron star merger simulations. Living Reviews in Computational Astrophysics, 10(1), Article 1. https://doi.org/10.1007/s41115-023-00019-9

Image Credits: AI Generated

DOI: 10.1007/s41115-023-00019-9

Keywords: neutron star mergers, turbulence, large-eddy simulation, gravitational waves, magnetohydrodynamics, Kelvin-Helmholtz instability, magnetic field amplification, subgrid models, numerical relativity, accretion disks, r-process nucleosynthesis, equation of state

The scale of the computational challenge is staggering. Temperature contrasts in astrophysical multiphase systems span roughly ten orders of magnitude, and the cold gas, while dense, can be scattered in tiny structures across enormous volumes. Observations suggest cold clouds in the circumgalactic medium may be as small as tens of parsecs. To fully resolve such structures across a Milky Way-sized halo would require more than a quadrillion resolution elements, a number so vast that no supercomputer on Earth comes close. As a result, cold gas quantities in typical cosmological simulations remain unconverged, and researchers have had to resort to a hierarchy of idealized experiments, from turbulent mixing layers to cloud-crushing simulations, each isolating one piece of the physics.

One of the central results the review highlights concerns the survival of cold clouds blasted by hot winds, the classic ‘cloud crushing’ problem. In the absence of cooling, a cold cloud embedded in a supersonic wind is shredded by Kelvin-Helmholtz and Rayleigh-Taylor instabilities on a characteristic crushing time. Worse, the drag time needed to accelerate the cloud is far longer than the destruction time, giving rise to the long-standing ‘entrainment problem’: how can cold gas be flung to hundreds or even thousands of kilometers per second in galactic winds if it should be destroyed before it can accelerate? The answer, it turns out, lies in radiative cooling. When mixed gas at the interface cools faster than the cloud is disrupted, hot gas condenses onto the cold phase, allowing clouds not only to survive but to grow in mass as they are carried along.

This insight has been crystallized into a simple survival criterion: clouds endure if the cooling time of the mixed gas is shorter than their destruction time, which can be recast as a minimum cloud size of roughly a few parsecs under typical wind conditions. Simulations show that surviving clouds grow continuously through cooling-driven mass transfer in their turbulent wakes, and that this same mixing efficiently transfers momentum, accelerating even dense molecular clouds entrained in galactic outflows. Magnetic fields, once heralded as a potential savior of the entrainment problem, help but are not sufficient on their own for the high density contrasts typical of real astrophysical clouds; combined with cooling, however, they shift the survival threshold by orders of magnitude.

The review also delves into thermal instability, the classic mechanism by which a hot medium can spontaneously fragment into a cold, clumpy phase. When radiative cooling increases as temperature drops, small density perturbations run away into dense clumps. In stratified halo atmospheres, precipitation occurs when the ratio of cooling time to free-fall time falls below a critical value of order ten, a criterion modified by turbulence, halo rotation, magnetic fields and cosmic rays. A related and still contentious question is whether cooling clouds ‘shatter’ into a characteristic scale of tiny fragments or instead undergo a violent pulsation dubbed ‘splattering’ before fragmenting, with recent three-dimensional simulations tending to favor the latter picture.

Scaling up, the review surveys supernova-driven bubbles, stratified ‘tall box’ simulations of galaxy disks, and fully global models of dwarf and Milky Way-mass galaxies. A consistent picture emerges: most outflowing mass travels in the warm phase at around 10,000 Kelvin, while most of the energy is carried by the hot, million-degree gas. Hot gas mass loading factors hover near 0.1 across a wide range of star formation rates, and warm outflows in massive galaxies tend to fall back as fountain flows rather than escaping. Including cosmic rays transforms these results, converting fountains into steady, cooler, denser winds that can double the outflowing mass and substantially reshape the circumgalactic medium.

At the largest scales, the review examines how simulations handle the circumgalactic and intracluster media. In cluster cores, jets from supermassive black holes stir turbulence that triggers local thermal instability, producing ‘chaotic cold accretion’ in which cold filaments rain onto the central galaxy and feed the black hole in a self-regulating cycle. In cosmological zoom-in simulations, a recent revolution has come from ‘super-Lagrangian’ refinement schemes that boost resolution specifically in the halo, reaching below 100 parsecs in the circumgalactic medium. These enhanced-resolution models consistently show more and smaller cool clouds, higher covering fractions of cool gas, and non-converged cloud mass functions, confirming that cold gas structure in halo simulations is far from fully resolved.

What emerges most clearly from this comprehensive synthesis is that the diverse simulation approaches, from idealized mixing layers to full cosmological models, are not competitors but complementary layers of a single framework. Small-scale experiments provide the physical intuition, survival criteria and subgrid prescriptions that large-scale simulations need; large-scale simulations in turn supply the realistic boundary conditions, pressures and turbulence levels under which the small-scale physics operates. The ultimate arbiter, the authors stress, is observation, and connecting simulations to real spectra, emission maps and absorption measurements through radiative transfer remains one of the field’s most demanding tasks.

The challenges ahead are formidable: achieving numerical convergence in multiphase diagnostics, capturing the interplay of magnetic fields, conduction, viscosity and cosmic rays, and resolving the critical scales that govern whether cold gas survives, grows or shatters. But the trajectory is clear. GPU-accelerated codes, adaptive refinement targeted at cooling lengths, and a maturing theoretical framework are converging on a unified picture of the multiphase Universe, one simulation at a time.

Subject of Research: Numerical simulations of multiphase gas dynamics in the interstellar, circumgalactic and intracluster media

Article Title: Simulations of multi-phase gas in and around galaxies

Article References: Gronke, M., & Schneider, E. E. (2026). Simulations of multi-phase gas in and around galaxies. Living Reviews in Computational Astrophysics, 12(1), Article 2. https://doi.org/10.1007/s41115-026-00025-7

Image Credits: AI Generated

DOI: 10.1007/s41115-026-00025-7

Keywords: multiphase gas, galaxy simulations, circumgalactic medium, thermal instability, galactic winds, cloud crushing, turbulent mixing layers, computational astrophysics, radiative cooling, cosmic rays, supernova feedback, Kelvin-Helmholtz instability