The research, led by Alexandros Palatos-Plexidas of the von Karman Institute for Fluid Dynamics together with colleagues at the Vrije Universiteit Brussel, the Royal Meteorological Institute of Belgium, and partner institutions, focuses on the 3.7 gigawatt Belgian-Dutch offshore wind farm cluster in the Southern Bight of the North Sea. This dense collection of neighboring wind farms is the largest offshore cluster in operation today, which makes it a prime natural laboratory for studying how wind farms interact with each other and with the atmosphere above them. The team ran the Weather Research and Forecasting model, known as WRF, at a horizontal resolution of one kilometer for three full years, from 2021 through 2023, a configuration that is both unusually long and unusually fine for offshore wake research.

The technical heart of the study is the Fitch wind farm parameterization scheme, the most widely used method for representing wind turbines inside mesoscale weather models. Because a one-kilometer grid cell cannot resolve individual turbines, the scheme imposes a momentum sink on the mean flow within cells containing turbines, converting a fraction of the extracted kinetic energy into electricity and the remainder into turbulent kinetic energy. The researchers ran two otherwise identical simulations side by side within a single model run, one with the wind farms switched on and one without them, allowing the wake effect to be isolated as the normalized difference between the two wind speeds. The model was initialized and driven at its boundaries by ERA5 reanalysis data at 30-kilometer resolution, with three nested domains stepping down from 9 to 3 to 1 kilometer, and 80 vertical levels densely packed in the lowest few hundred meters of the atmosphere.

What sets this study apart is the observational benchmark. Four vertical profiling lidars, operated by different institutions, surround the cluster in a nearly perfect alignment along the prevailing southwesterly wind direction. The Westhinder lidar, installed on a survey platform roughly 40 kilometers southwest of the cluster, samples the incoming freestream flow. The Borssele B lidar, mounted on a TenneT grid platform inside the cluster, records what happens within the farms themselves. Two further lidars on the Europlatform and Lichteiland Goeree platforms to the northeast capture the far wake. Together they provide an unusually complete picture of upstream, intra-farm, and downstream conditions, all interpolated to a common reference hub height of 107 meters, typical of the turbines in the region.

The results show that switching on the wind farm parameterization substantially improves model accuracy where it matters most: inside and immediately downstream of the turbines. At the Borssele B location, under the dominant southwesterly flow, the wind farm simulation reduced the wind speed bias by about 82 percent compared with the no-farm run and by about 73 percent compared with raw ERA5 data. The Earth Mover’s Distance, a statistical measure of how well two distributions match, improved by roughly 74 percent and 65 percent respectively at the same site. Wind direction bias at Borssele B also dropped by about half when the turbines were represented. Across the wider domain, however, the picture is more nuanced: the Fitch scheme introduces an additional wind speed deficit downstream of the cluster, so the no-farm run can appear slightly more accurate at far-wake locations, a pattern consistent with earlier studies showing that the scheme tends to produce prolonged wakes.

Atmospheric stability emerged as a crucial controlling factor. Using the Obukhov length, a standard measure of whether buoyancy or wind shear dominates turbulence production in the boundary layer, the team classified conditions into seven categories and evaluated model performance in each. Very unstable conditions, in which buoyancy drives strong vertical mixing, prevailed over the three-year period, particularly in January and during late summer and autumn. The model performed best under near-neutral and moderately stable conditions, with correlation coefficients above 0.80, while accuracy degraded under the most extreme stratification, where correlation fell to between 0.69 and 0.77 and the root-mean-square error climbed. Notably, the wind farm simulation outperformed the no-farm run at the intra-cluster site across all stability regimes, even under the very stable conditions known to produce the longest and most persistent wakes.

The analysis also revealed striking directional asymmetries in wake behavior. Under southwesterly winds, the strongest wind speed deficit originates in the densely built Belgian wind farms, where turbine density averages 10 to 12 megawatts per square kilometer, and remains nearly unchanged as the flow crosses the more widely spaced Borssele zone, where density is only 4 to 5 megawatts per square kilometer. Under northeasterly winds the sequence reverses: the deficit builds gradually through Borssele and then intensifies sharply as the flow enters the dense Belgian cluster. At wind speeds above 11 meters per second, the maximum simulated deficit reached about 2.75 meters per second under southwesterly flow but about 4.06 meters per second under northeasterly flow. The study also documented blockage effects, the modest slowing of wind upstream of the farms, extending roughly 8 to 10 kilometers ahead of the front row under southwesterly conditions and about 5 kilometers under northeasterly flow, with the effect weakening as wind speeds increase.

To test the model against an entirely independent data source, the team compared simulated wake signatures with synthetic aperture radar images from the European Space Agency’s Sentinel-1 satellites. These radar instruments measure the roughness of the sea surface, which reflects centimeter-scale waves generated by the instantaneous wind stress, allowing wind speed at 10 meters height to be retrieved at 500-meter pixel resolution. For four carefully selected wake events, the wind farm simulation captured the larger-scale structure and extent of the wakes visible in the satellite images, including one case in which a prolonged wake extended more than 50 kilometers downstream under stable-neutral stratification. Quantitatively, the mean absolute error along transects through the cluster improved by 81 percent in the intra-farm region and 85 percent downstream compared with the no-farm simulation in the best-captured event.

The study is candid about its limitations. The model inherits a systematic negative wind speed bias from its ERA5 boundary conditions, which propagates into both simulations, and some of the apparent accuracy of the no-farm run likely reflects error cancellation with that biased input. The choice of the turbulent kinetic energy coefficient in the Fitch scheme remains debated; the team tested values of 0, 0.25, and 1 in a six-month sensitivity analysis and found that no single value consistently outperformed the others, settling on the WRF default of 0.25. The Obukhov length itself becomes uncertain under strongly stable conditions, when Monin-Obukhov similarity theory is known to break down. The authors suggest that future work should explore alternative parameterizations, such as the explicit wake parameterization scheme, and different boundary layer physics configurations.

For the offshore wind industry, the implications are significant. Wakes are the dominant driver of power losses in offshore wind farms, cutting energy extraction by 10 to 20 percent relative to lone-standing turbines, and as more clusters are built in each other’s vicinity, neighboring farms will increasingly sit inside each other’s wakes. Reliable multi-year simulations at kilometer scale, validated against real measurements, are essential for siting decisions, power forecasting, and estimating how future installations will affect existing ones. This three-year experiment demonstrates that a well-configured mesoscale model with a wind farm parameterization can do that job credibly, particularly within the waked regions themselves, while also mapping out precisely where the remaining uncertainties lie. As Europe races to multiply its offshore wind capacity, tools like this will help ensure that the next generation of wind farms is planned with a clear-eyed view of the atmosphere they must share.

Subject of Research: Long-term validation of mesoscale wind farm wake modeling in the North Sea using lidar and satellite radar observations

Article Title: Assessing the accuracy of a 3-year high-resolution mesoscale wind farm wake simulation with lidar and satellite radar data

Article References: Palatos-Plexidas, A., Gremmo, S., van Beeck, J., De Cruz, L., & Munters, W. (2026). Assessing the accuracy of a 3-year high-resolution mesoscale wind farm wake simulation with lidar and satellite radar data. Wind Energy Science, 11(9), 3555-3585. https://doi.org/10.5194/wes-11-3555-2026

Image Credits: AI Generated

DOI: 10.5194/wes-11-3555-2026

Keywords: offshore wind energy, wind farm wakes, WRF model, Fitch parameterization, North Sea, lidar, synthetic aperture radar, atmospheric boundary layer, atmospheric stability, mesoscale modeling, wind speed deficit, Sentinel-1

The research team, led by Hui Huang and Cong Zhang, prepared three mixtures under an identical binder–aggregate–water framework: a plain mortar control, a PP-fiber-reinforced composite, and a PVA-fiber-reinforced composite. Both fiber types were 6 millimeters long and added at a volume fraction of 1.0 percent, so that fiber dosage would not confound the comparison. The fibers themselves, however, are chemically and mechanically very different. The PP fiber, with a diameter of 20 micrometers, has a tensile strength of 800 megapascals and an elastic modulus of 5 gigapascals, and its surface is hydrophobic. The finer PVA fiber, at 14 micrometers, is roughly twice as strong at 1,800 megapascals and nearly three times stiffer at 14 gigapascals, and its hydrophilic chemistry bonds more readily with cement hydration products. Those differences turned out to matter enormously.

The specimens, 70.7-millimeter cubes, were cured for 28 days and then subjected to a rapid water-freezing and water-thawing regime in which temperatures swung between −18 °C and 5 °C every four hours, with the samples fully immersed. Measurements were taken after 0, 25, 50, 75, 100, 125, and 150 cycles. Before exposure, the plain mortar was actually the strongest material, at 65.03 megapascals of compressive strength, compared with 61.92 megapascals for the PVA composite and 54.61 megapascals for the PP composite. But strength at the start is not the same as strength that lasts.

After 150 freeze–thaw cycles, the ranking had reversed in the way that matters for durability. The fiber-reinforced composites retained 82.29 percent (PP) and 84.50 percent (PVA) of their initial compressive strength, while the unreinforced mortar retained only 66.45 percent. Mass loss told a parallel story: the mortar reached 5 percent mass loss by 125 cycles through continuous surface scaling, whereas both fiber mixtures degraded more slowly in a three-stage pattern—slow loss in the first 50 cycles, acceleration between 50 and 100 cycles as cracks connected, and a slowdown thereafter. Even after 150 cycles, the fibers still reduced surface material loss by more than 59 percent relative to plain mortar, with PVA consistently outperforming PP.

Perhaps the most striking finding was that stiffness, not strength, is the more sensitive fingerprint of freeze–thaw damage. The apparent elastic modulus, derived from the slope of the compressive stress–strain curve between 10 and 30 percent of peak stress, fell by 32.9 percent for PP-FRCC and 29.2 percent for PVA-FRCC over 150 cycles—nearly double the corresponding strength losses. Because the modulus reflects the load-transfer continuity of the internal microstructure, it registers the accumulation of pores, microcracks, and debonded interfaces long before catastrophic strength loss appears. For engineers assessing aging infrastructure, this suggests that stiffness-based or non-destructive measurements could serve as an early-warning system for frost damage.

Scanning electron microscopy revealed why the two fibers behaved differently. Using calibrated image analysis with two semi-quantitative descriptors—a visible defect area ratio and an interfacial deterioration ratio—the researchers quantified what they saw in the micrographs. The visible defect area ratio climbed from 1.8 to 8.2 percent in mortar, from lower starting values to 6.8 percent in PP-FRCC and 5.6 percent in PVA-FRCC. More telling was the interface: the fraction of visibly deteriorated fiber–matrix boundary rose from 3.0 to 21.2 percent for PP-FRCC but only from 2.7 to 17.4 percent for PVA-FRCC. The hydrophobic PP surface, chemically stable and inexpensive, gradually separated from the matrix under repeated hydraulic and crystallization pressures, while the hydrophilic PVA fiber maintained more continuous contact with surrounding hydration products.

Those microscopic differences echoed upward into failure behavior. Under splitting tensile loading, PVA-FRCC specimens displayed primary or deflected splitting cracks in 17 of 21 observations (81.0 percent), whereas PP-FRCC showed localized or branched damage features in 15 of 21 (71.4 percent). The tensile-to-compressive strength ratio, a marker of balanced degradation, stayed remarkably stable for PVA-FRCC—fluctuating within a coefficient of variation of just 0.83 percent—while mortar’s ratio swung the most. In compression, fiber-bridged cracking kept the reinforced cubes intact even after 150 cycles, while the plain mortar failed brittly with severe spalling and a conical crushing zone at its core.

To connect these observations mechanically, the team built a three-dimensional mesoscale model in ABAQUS, representing the composite as a cementitious matrix, randomly distributed equivalent fibers, and an equivalent pore phase whose volumetric expansion stands in for freeze–thaw damage. Calibrated against the measured compressive response at each cycle stage, the model reproduced the monotonic strength decline well at early and intermediate stages, with discrepancies under about 4 percent through 75 cycles. By 150 cycles the gap widened to roughly 17 percent for both composites—a limitation the authors attribute to the simplified pore-expansion representation and the absence of explicit fiber–matrix bond-slip and pull-out. Still, the simulated damage fields were revealing: equivalent strain, stiffness degradation, and compressive damage, initially scattered around isolated weak regions, progressively coalesced into connected damage paths as freeze–thaw pre-damage accumulated, mirroring the experimentally observed shift from distributed fine cracking to localized through-cracks.

Two-way analysis of variance confirmed that material type, cycle number, and their interaction significantly shaped mass loss, compressive strength retention, and stiffness retention, with strikingly large effect sizes—partial eta squared above 0.9 for the cycle effect on mass loss. For splitting tensile strength retention, the interaction was not significant, indicating that tensile degradation followed overall material and cycle effects rather than a material-specific trajectory. Together, the statistics reinforce the central message: fiber reinforcement does not merely slow damage, it changes the pathway by which deterioration proceeds.

The practical implications reach cold-region roads, bridges, repair overlays, and any concrete that may sit water-saturated through winter. Adding just 1 percent by volume of either fiber substantially curbed surface scaling and strength loss, with PVA offering slightly better residual strength, stiffness retention, and interfacial stability under this particular mixture design, while PP delivered comparable protection at lower cost. The authors are careful to note that the comparison is bounded by their specific mix proportions, cube geometry, and exposure regime, and that dosage optimization, salt frost exposure, flexural testing, and quantitative porosity measurements remain for future work. Even so, the study delivers a rare, unified account of frost damage—from the swelling pore to the debonding fiber to the cracking cube—and a compelling case that the humble fiber–matrix interface may be the most important square micrometers in frozen concrete.

Subject of Research: Freeze–thaw damage evolution and durability of polypropylene- and polyvinyl alcohol-fiber-reinforced cementitious composites

Article Title: Freeze–thaw damage evolution of PP- and PVA-fiber-reinforced cementitious composites: Mechanical degradation, interfacial deterioration and mesoscale modeling

Article References: Huang, H., Zhang, C., Liu, X., Xu, X., Jia, B., Gu, S., & Wang, W. (2026). Freeze–thaw damage evolution of PP- and PVA-fiber-reinforced cementitious composites: Mechanical degradation, interfacial deterioration and mesoscale modeling. Case Studies in Construction Materials, 25, Article e06594. https://doi.org/10.1016/j.cscm.2026.e06594

Image Credits: AI Generated

DOI: 10.1016/j.cscm.2026.e06594

Keywords: freeze–thaw cycles, fiber-reinforced cementitious composites, polypropylene fibers, PVA fibers, concrete durability, interfacial deterioration, mesoscale modeling, elastic modulus degradation, scanning electron microscopy, compressive strength retention, cold-region infrastructure, damage localization

The study, published in Theoretical and Applied Climatology, pitted the Weather Research and Forecasting model, known universally as WRF, against its younger rival, the Model for Prediction Across Scales–Atmosphere, or MPAS-A. Both were developed at the National Center for Atmospheric Research in collaboration with the U.S. Department of Energy, but they represent fundamentally different philosophies of how to chop the atmosphere into computable pieces. WRF, the long-reigning workhorse of operational forecasting in Brazil and worldwide, divides space into a regular grid of quadrilateral cells using finite-difference mathematics. MPAS-A instead wraps the planet in an unstructured Voronoi mesh — a honeycomb of hexagonal cells solved with finite-volume methods — that can transition smoothly from coarse global coverage to razor-sharp regional detail without the awkward seams of nested grids.

That architectural difference matters because the São Paulo storm was a textbook case of scale interaction. Brazilian Navy synoptic charts from the day show a high-pressure system of 1026 hPa parked over the South Atlantic, east of the state, while a deepening low-pressure area associated with a trough near southern Brazil and Paraguay intensified rapidly. The clash between warm pre-frontal air and cold post-frontal air, supercharged by daytime heating and oceanic moisture, ignited a cluster of cumulonimbus clouds. GOES-16 satellite imagery shows isolated convective cores forming between 5:30 and 6:30 in the evening, organizing into a full-blown convective system by 8:30, and then sweeping out to sea after 9:00. A pre-frontal low-level jet, channeled by valleys and hills toward the coast, added fuel to the fire.

To see which model could capture this chaos, the team, led by Rosales Aylas Georgynio Yossimar of SENAI CIMATEC’s Supercomputing Center in Salvador, ran both models at 3-kilometer resolution over a domain covering southeastern Brazil and a generous slice of the South Atlantic. WRF used its classic two-way nested setup at 9 and 3 kilometers, while MPAS-A employed a quasi-uniform mesh at roughly 3 kilometers across the entire domain. Both simulations shared 41 vertical levels and equivalent physics suites — the tropical package in WRF and the mesoscale reference package in MPAS-A — and both were initialized and driven by the ERA5 reanalysis from the European Centre for Medium-Range Weather Forecasts. Three separate experiments, initialized at different times before the event, tested how forecast skill decayed with lead time.

The verdict on the big picture was encouraging: both models successfully reproduced the synoptic skeleton of the event, including the trough, the frontal organization, and the intensification of low-level winds. At the 850 hPa level, roughly one to two kilometers above the surface, both simulations captured wind speeds exceeding 25 meters per second — Beaufort force 10, a strong gale — sweeping over the central and northern parts of São Paulo state near Bauru before the wind corridor shifted southeastward and weakened after 8:00 in the evening. WRF painted broader wind fields, while MPAS-A drew more compact, concentrated cores of extreme wind, a signature the authors link to its finite-volume formulation on the Voronoi mesh.

Beneath that broad agreement, however, the models diverged in revealing ways. Against ERA5, MPAS-A showed stronger spatial correlation over most of the analyzed period, particularly over the ocean, suggesting its unstructured grid handles mesoscale gradients and frontal features with more numerical grace. Yet higher spatial correlation did not translate into smaller errors: during the storm’s intensification phase, MPAS-A’s mean absolute error and root mean square error actually grew, hinting that the model may have over-intensified wind maxima or displaced the storm’s core. WRF, meanwhile, showed a more heterogeneous bias pattern, overestimating winds over land while underestimating them over the southern ocean sector. The team is careful to note that these differences cannot be pinned on mesh geometry alone — dynamics, physics coupling, and land-surface representation all conspire.

One unexpected confound emerged from the ground up. Because of limitations in MPAS-A’s preprocessing tools, the two models were fed different land-use datasets: WRF received the current 2019 MapBiomas classification of Brazil, while MPAS-A relied on the older MODIS-IGBP dataset from the early 2000s. Grasslands covered about 35 percent of WRF’s domain but only 7 percent of MPAS-A’s, with corresponding shifts in savanna and cropland fractions. Since vegetation categories control roughness length, albedo, and evapotranspiration — and therefore the surface energy balance that drives boundary-layer turbulence — this discrepancy adds a layer of ambiguity to any model-versus-model comparison, and the authors flag it as a caveat that future studies must untangle.

Comparisons against Brazil’s INMET weather stations exposed the models’ Achilles’ heel: complex topography. At Campos do Jordão, perched at 1,628 meters in the Serra da Mantiqueira, WRF produced its worst errors, with root mean square errors and mean biases exceeding 6 meters per second. The reason is almost cartographic: WRF’s nearest grid point sat closer to the mountain slope, where terrain-induced flow acceleration inflated its wind estimates, while MPAS-A’s representative point fell in the calmer urban core. At Cachoeira Paulista, tucked into the Paraíba Valley at the foot of the same range, similar problems appeared. Near the storm center around Bauru, by contrast, both models performed respectably, with errors under 3 meters per second and biases below 2.

Temperature told a story of mirror-image biases. WRF tended to overestimate 2-meter air temperature, sometimes by more than 1.5 degrees Celsius, while MPAS-A generally underestimated it, especially during daytime heating. Yet both models faithfully reproduced the diurnal cycle — the pre-storm warming on October 10, the abrupt frontal cooling on the 11th, and the recovery on the 12th — though they sharpened the storm-driven temperature drop more than observations showed. The most troubling statistic came from hourly wind correlations: no model exceeded 0.78 at any station, roughly 63 percent of stations fell between 0.2 and 0.6, and some correlations were near zero or negative, exposing a fundamental struggle to capture hour-by-hour wind variability during the event.

The authors attribute these timing failures to a familiar culprit: the sparse observational network of the Southern Hemisphere, which degrades the initial conditions fed into every model, compounded by uncertainties in turbulence parameterization and the use of hourly mean winds rather than true gust measurements. Their prescription is clear — better physics, denser observations, and complementary techniques such as data assimilation and statistical post-processing. As forecasting agencies worldwide weigh a transition from WRF’s rigid squares to MPAS-A’s flexible hexagons, this storm-soaked Brazilian case study offers a sobering reminder: the grid may be getting smarter, but the atmosphere still keeps some of its wildest moments just beyond the model’s grasp.

Subject of Research: Comparative simulation of an extreme wind gust event in São Paulo using the WRF and MPAS-A atmospheric models

Article Title: Simulation of an anomalously high wind gust event in São Paulo: A comparison of the MPAS-A and WRF models

Article References: Yossimar, R. A. G., da Silveira, P. W. M., dos, S. T. S., Duarte, J. W., de, L. F. J. L., Cotta, W. A. L., Cavalcante, A. A., Rodrigues, S. A., da Silva, R. D. N., & Martins, M. D. (2026). Simulation of an anomalously high wind gust event in São Paulo: A comparison of the MPAS-A and WRF models. Theoretical and Applied Climatology, 157(10), Article 644. https://doi.org/10.1007/s00704-026-06537-9

Image Credits: AI Generated

DOI: 10.1007/s00704-026-06537-9

Keywords: WRF, MPAS-A, wind gusts, extratropical cyclone, São Paulo, numerical weather prediction, mesoscale modeling, Voronoi mesh, ERA5 reanalysis, boundary layer, severe weather, Brazil

The method rests on a striking wave phenomenon. When an ultrasonic pulse is launched from a transmitter on one side of a surface-breaking crack and detected by a receiver on the other side, the phase of the first arriving wave can abruptly flip by 180 degrees as the receiver is moved away from the crack. This reversal happens because the wave diffracted from the crack tip changes its vibration polarity once a critical geometric condition is met among the transmitter, the crack tip, and the receiver. The critical folding angle that governs the flip is tied to the Poisson’s ratio of the material, and for typical concrete values between 0.20 and 0.25 the angle is close to 90 degrees. In that ideal case, the crack depth is simply the geometric mean of the distances from the transmitter and the receiver to the crack, a relationship so clean that it turns a subtle waveform feature into a direct ruler for hidden damage.

What sets the new work apart from earlier numerical studies is its refusal to treat concrete as an idealized, uniform solid. At the mesoscale, concrete is a multiphase composite of randomly distributed coarse and fine aggregates embedded in mortar, and those stiff, irregular inclusions scatter stress waves in complicated ways that could in principle corrupt any measurement based on wave timing and polarity. The researchers, led by Zeyu Li and Hongbing Chen, built two-dimensional finite element models populated with randomly placed aggregates of three distinct shapes: circular, elliptical, and polygonal. Aggregate positions were generated by Monte Carlo sampling with strict non-overlap rules, and the gradation followed an ideal maximum-density curve converted to two dimensions with the Walraven method. Aggregate shapes were synthesized parametrically, with ellipses derived by scaling and rotating base circles and polygons grown by an iterative skeleton-extension algorithm that expands a convex quadrilateral until its area reaches 80 percent of the corresponding circle.

To make these computationally demanding models practical, the team introduced a localized perturbation algorithm for aggregate placement. Instead of discarding a newly generated aggregate the moment it overlaps an existing one, the algorithm attempts up to ten small local repositioning moves before rejecting the candidate. On a representative 100 by 100 millimeter specimen, this simple change cut the average aggregate generation time from 281.61 seconds to 53.93 seconds, a reduction of roughly 80 percent, without sacrificing the randomness of the packing. That kind of efficiency gain matters because wave propagation simulations in heterogeneous media are expensive, and the researchers needed to run many configurations spanning crack depths of 3, 6, and 9 centimeters across all three aggregate shapes.

The baseline simulations used a homogeneous 1000 by 300 millimeter concrete slab with a 1 millimeter mesh and a 0.25 microsecond explicit time step, fine enough to resolve at least ten elements per wavelength of the 50 kilohertz excitation signal. A half-cycle sine pulse was applied at a point fixed at a distance equal to the crack depth from the crack, while receiving points marched away from the crack in 10 millimeter steps. In the intact model, no phase change ever appeared, and the arriving waves carried markedly larger amplitudes. In the cracked models, the reversal occurred exactly where the geometry predicted: the estimated depth ranges bracketed the true depths of 3, 6, and 9 centimeters in every case, with the upper bound landing precisely on the actual depth each time.

The team then confronted two complications that plague field inspections. First, they filled a 6-centimeter-deep, 1-millimeter-wide crack with sand, soil, or water, assigning realistic elastic and acoustic properties to each filler. Because all three materials are low-impedance, low-velocity media relative to concrete, they allow partial stress-wave transmission across the crack, weakening the free-surface constraint at the crack tip and shifting the reversal point slightly toward the crack face. The result was a modest underestimation of depth by about 8.67 percent, and notably the identified depth range was identical for all three fillers, because the filler-induced shift was smaller than the spatial resolution set by the measurement point spacing. Second, they compared the mesoscale models against the homogeneous baseline and found that although aggregates injected local stress fluctuations and complicated the received waveforms, the critical geometric condition for the first phase reversal remained untouched. Every aggregate shape, from smooth circles to jagged polygons, produced the same accurate depth estimates as the uniform model.

The decisive test came in the laboratory. The researchers cast a large C40 concrete specimen measuring 1500 by 1500 by 300 millimeters, embedding reinforcement only in its lower section so the unreinforced top surface could host twelve artificial cracks ranging from 2 to 20 centimeters deep. The cracks were pre-formed with 1-millimeter-thick PVC plates coated in light oil to prevent bonding, and three 10-centimeter cracks were deliberately filled with water, soil, and sand to replicate the infiltration that outdoor and underground structures inevitably suffer. The measurement protocol was straightforward: the excitation point started 2 centimeters from the crack, the receiver stepped away in 2-centimeter increments, and then the excitation itself was advanced, building a grid of transmitter-receiver pairs spanning the crack region until the phase flip revealed itself.

The experimental results vindicated the method across nearly the entire depth range. Cracks from 6 to 16 centimeters and the 20-centimeter crack were all correctly bracketed within their estimated intervals, and accuracy improved as cracks got deeper, exactly as the theory suggests. The failures were instructive rather than fatal. The 2-centimeter and 18-centimeter cracks fell into adjacent intervals, and the 4-centimeter crack could not be reliably estimated at all, because the physical dimensions of the transducers and impact source prevent them from getting close enough to shallow cracks and blur the phase reversal in the recorded waveform. Low first-arrival energy combined with surface roughness further muddied the signal at some receiver positions. For the filled cracks, the outcome mirrored the simulations perfectly: the dry, water-filled, soil-filled, and sand-filled 10-centimeter cracks all yielded the same estimated depth range, from 10.00 to 10.95 centimeters, confirming that common environmental fillers do not defeat the technique.

The broader significance of this work lies in its honesty about real materials and real conditions. Most nondestructive evaluation methods are validated on dry, clean, laboratory-pristine cracks, yet structures in the field are riddled with cracks infiltrated by rainwater, soil, and debris, which can render timing-based ultrasonic readings ambiguous. Thermal methods are hostage to sunlight, wind, and humidity; optical methods see only surfaces; ground-penetrating radar demands elaborate arrays. The phase reversal method, by contrast, needs only a transmitter, a receiver, and a geometric interpretation of the first arriving wave, and it now carries evidence that it tolerates both aggregate heterogeneity and crack filling. The authors are candid about the limits of the current study: their mesoscale model includes only aggregate and mortar, omitting the interfacial transition zone that is known to influence wave behavior, and only vertical surface-breaking cracks were considered. Extending the approach to inclined cracks and adding the interfacial zone are the declared next steps. Even so, the demonstration that a 180-degree flip in a wave’s first arrival can survive jagged aggregates, standing water, and packed soil marks a meaningful step toward crack depth measurements that work where infrastructure actually lives, outside and underground, in the rain.

Subject of Research: Non-destructive ultrasonic detection of surface crack depth in concrete using the initial wave phase reversal method, validated with mesoscale numerical models and experiments

Article Title: Crack depth detection in concrete by initial wave phase reversal method: Mesoscale numerical analysis and experimental validation

Article References: Li, Z., Gokarna, C., Li, Y., & Chen, H. (2026). Crack depth detection in concrete by initial wave phase reversal method: Mesoscale numerical analysis and experimental validation. Case Studies in Construction Materials, 25, Article e06573. https://doi.org/10.1016/j.cscm.2026.e06573

Image Credits: AI Generated

DOI: 10.1016/j.cscm.2026.e06573

Keywords: concrete, crack depth, non-destructive testing, ultrasonic testing, phase reversal, mesoscale modeling, finite element analysis, random aggregate model, stress waves, structural health monitoring, crack filling materials, experimental validation