The research, carried out by Vay Siu Lo, Huy Hoang Truong, and Thien Tich Truong and funded by Vietnam National University Ho Chi Minh City, focuses on a wavemaker type that is common in small laboratories but awkward in theory. Unlike the classical piston paddle, which pushes water horizontally with well-understood linear relations between stroke and wave height, a plunger oscillates vertically, plunging into and out of the water. When the box-shaped plunger moves downward it displaces a volume of water that must go somewhere, raising the free surface in front of it; when it rises, water backfills and the surface dips. That volumetric intuition is the seed of the team’s analytical predictor.

The authors formalized this intuition using mass conservation. By equating the volume swept by the submerged plunger with the volume of water contained in the wave crest over half a wavelength, and by evaluating that wave volume with linear wave theory, they arrived at a remarkably compact expression: the generated wave height equals the plunger stroke multiplied by the wave number and the plunger side length. The wave number itself follows from the standard dispersion relation, with the wave frequency locked to the plunger’s oscillation frequency. In other words, with nothing more than the stroke amplitude, the oscillation period, the water depth, and the plunger geometry, an engineer can estimate the resulting wave height with a few lines of algebra.

Crucially, the team did not simply assert that this formula works. They verified that every operating condition they tested falls within the linear wave regime, using Le Méhauté’s classical diagram for selecting wave theories, and they validated the expression against both experiment and simulation. For the smallest stroke amplitude of 0.045 meters, the discrepancy between the analytical prediction and the CFD simulation was just 1.72 percent. As the stroke grew toward 0.15 meters, the error climbed to roughly 7 percent, a trend the authors attribute to the operating point drifting toward the boundary of the linear regime, where finite-tank reflections, viscous effects, and weak nonlinearities begin to matter. Even at the largest stroke, the simple sinusoidal theoretical waveform tracked the simulated free-surface history closely.

The experimental side of the study is a lesson in doing careful science on a modest budget. The team built a narrow flume designed to promote one-directional wave propagation and suppress three-dimensional effects, with the box-shaped plunger driven by a mechanical actuation system in prescribed harmonic motion. Free-surface elevation was measured with an HC-SR04 ultrasonic sensor paired with a lightweight floating ball constrained by vertical guides, a passive tracking arrangement that stabilizes the reading against surface ripples. The system was zero-point calibrated against the still-water level before each test, and the manufacturer-specified measurement uncertainty of plus or minus 3 millimeters was carried explicitly into the validation analysis.

On the computational side, the researchers constructed a two-dimensional numerical wave tank in ANSYS Fluent that reproduces the actual finite length of the physical facility, including the downstream oscillating water column chamber geometry. The two-phase water-air flow is captured with the volume of fluid method, the incompressible Navier-Stokes equations are solved on a pressure-based transient solver, and the plunger’s motion is imposed through a user-defined function driving a dynamic mesh with smoothing, layering, and remeshing. A mesh sensitivity study comparing grids of roughly 23,000, 39,000, and 60,000 elements showed that the medium and fine meshes produced nearly identical wave patterns, while the coarse grid introduced visible discrepancies, so the medium mesh was adopted as the practical compromise between accuracy and cost.

The validation result is striking given the deliberately realistic setup: the flume has no absorbing beach, so the simulation retained a stationary no-slip wall downstream to reproduce the reflected-wave environment of the real tank. Even so, the numerical and experimental wave signals agreed well in both amplitude and period, with a root-mean-square relative amplitude error of 6.5 percent over a 20-second steady-state window, and residual crest discrepancies traced to measurement noise and finite-tank effects, mitigated with a Kalman filter. A spectral analysis added a further layer of rigor: total harmonic distortion rose monotonically from 5.5 percent at the smallest stroke to 12.6 percent at the largest, confirming that the generated waves remained predominantly monochromatic and justifying the linear-theory machinery across the tested range.

The most forward-looking element is the physics-informed neural network, or PINN, a machine learning surrogate trained not on mountains of labeled data but on the governing equations themselves. The team formulated the problem in the frequency domain, enforcing Laplace’s equation in the fluid interior, the linearized free-surface Robin condition, the impermeable bottom, no-penetration on the plunger sides, the prescribed plunger velocity on its bottom face, and a Sommerfeld-type outgoing-wave condition at the outlet. The network, a fully connected multilayer perceptron with eight hidden layers of 96 neurons and tanh activations, was trained for 12,000 epochs with weighted collocation residuals, sampling different stroke and period combinations each iteration so that a single trained model can answer queries across the entire operating domain.

The surrogate’s performance against the validated CFD simulations is the headline number: for the four largest stroke cases the difference was below 2.14 percent, and for the largest stroke of 0.15 meters it was a mere 0.66 percent. Against the analytical predictor across both stroke-driven and period-driven cases, the mean error was about 6.5 percent with a worst case near 14 percent at the smallest amplitude. A hyperparameter sensitivity study showed the results were more sensitive to learning rate than to network depth or width, and training took between roughly four and five and a half hours on a common CPU, a modest investment compared with the transient two-phase CFD runs the surrogate is designed to replace during screening.

The payoff is a dense response surface mapping wave amplitude across the entire stroke-period plane, generated in seconds rather than by weeks of simulation, letting engineers zero in on candidate wavemaker settings before committing to high-fidelity runs or laboratory time. The authors are careful about scope: the analytical formula and the PINN are valid only within the linear regime, the surrogate models the dominant propagating wave rather than the full reflected field, and the workflow is configuration-specific rather than a new general wave theory. But the framework is extensible, with numerical quadrature able to handle higher-order wave volume terms and nonlinear constraints replaceable as training methods mature. For the coastal laboratories developing the next generation of oscillating water columns, point absorbers, and integrated wind-wave systems, this study offers something quietly transformative: a wave tank that, in effect, already knows what wave it is going to make.

Subject of Research: Calibration of plunger-type wavemaker wave generation in a numerical wave tank using experiments, CFD, analytical prediction, and a physics-informed neural network surrogate

Article Title: Experiment, simulation, analytical prediction and a PINN surrogate for generated wave analysis in a numerical wave tank with box-shaped plunger wavemaker type

Article References: Lo, V. S., Truong, H. H., & Truong, T. T. (2026). Experiment, simulation, analytical prediction and a PINN surrogate for generated wave analysis in a numerical wave tank with box-shaped plunger wavemaker type. Results in Engineering, 32, Article 113245. https://doi.org/10.1016/j.rineng.2026.113245

Image Credits: AI Generated

DOI: 10.1016/j.rineng.2026.113245

Keywords: numerical wave tank, plunger wavemaker, physics-informed neural network, wave energy, computational fluid dynamics, linear wave theory, volume of fluid method, oscillating water column, wave tank calibration, Le Méhauté diagram, machine learning surrogate, renewable energy

The core problem is deceptively simple. Environmental regulators have long required developers to assess not just the direct footprint of a project but the combined burden it adds to everything else pressing on an ecosystem, from shipping noise to climate-driven warming. Yet the review finds that for marine renewable energy, effects are typically measured one by one: the underwater noise of a turbine, the electromagnetic field of an export cable, the collision risk posed to fish and marine mammals by rotating tidal blades. What is rarely asked is how these effects stack on top of each other, on top of other industries, and on top of natural variability. The authors argue that without rigorous cumulative assessment, the industry could trigger unintended consequences that undermine the very sustainability goals, from clean energy to climate action and healthier oceans, that motivate it.

The marine environment makes this accounting uniquely hard. Unlike land, the ocean is fluid and dispersive, spreading impacts far beyond the physical footprint of any installation. Many marine species shift habitats across their life histories, making some ecosystems exquisitely sensitive to disturbance at particular stages. Add the accelerating pace of ocean industrialization, what researchers have called the blue acceleration, and the potential for cascading, unpredictable effects multiplies. The review emphasizes that cumulative effects can arise not only from multiple projects of the same kind, such as a cluster of wave energy converters, but from interactions across sectors: fisheries, shipping, tourism, offshore wind, and the overarching pressure of climate change. Effects may also appear only after a lag, or emerge only when a second activity unlocks the harm of the first.

Not all cumulative effects are additive, and this is where the science gets genuinely tricky. The review catalogues several interaction types. Additive effects are the intuitive case: the combined outcome equals the sum of the parts, and this pattern tends to dominate in physical systems. But ecosystems frequently behave nonlinearly. Synergistic effects occur when two stressors amplify each other, as when mortality from boat strikes and fishing combines with climate-driven heat stress to push a population below the threshold where it can reproduce. Dominant effects arise when one process overwhelms everything else, such as sediment smothering a seagrass meadow regardless of how ideal other conditions are. Antagonistic effects, where stressors dampen one another, can even flip an expected harm into a benefit. Two decades of research suggest these nonlinear outcomes are the norm in ecosystems, not the exception, yet most assessment tools in routine use cannot represent them.

The history of the field explains some of the current awkwardness. Cumulative effects entered regulatory consciousness in the 1970s and 1980s as terrestrial industrialization accelerated, but progress stalled for years because ecosystem-scale analysis demanded large datasets and heavy computation. By the 1990s, tiered risk assessment had become standard practice, beginning with qualitative risk tables before progressing to quantitative methods. Marine cumulative assessment surged from the 2000s, often driven by European policy such as the Marine Strategy Framework Directive. Of 154 marine assessments reviewed in one earlier synthesis, the majority simply combined geographic information system maps of overlapping human activities with expert judgments about how those activities interact. Expert elicitation remains a workhorse, and the review notes it is currently the most widely used route for bringing Indigenous knowledge into cumulative effects assessment, though repeatability remains a persistent weakness that structured techniques such as the Delphi method try to address.

The review organizes fifteen assessment methods along a gradient of increasing computational complexity. At the simple end sit interaction matrices and map-based overlays, including the weighted cumulative pressure mapping approach popularized by a landmark 2008 global study of human impacts on the ocean, variants of which account for more than half of all published assessments. These methods are transparent and digestible, which explains their appeal to stretched regulatory agencies, but they treat effects as linear and additive and cannot capture feedbacks or indirect pathways. In the middle tier are qualitative system models, notably loop analysis using signed digraphs, which have been applied to offshore wind in the Bay of Seine and elsewhere. These networks of positive and negative links between pressures and ecosystem components can be tested for stability and probed with perturbation analysis, and they pair naturally with Bayesian belief networks that attach conditional probabilities to each causal link.

At the demanding end lie fully dynamic numerical models: models of intermediate complexity focused on a handful of interacting species, and whole-of-system ecosystem models that track age-structured populations or biomass pools across space and time. Originally built for fisheries and climate research, these platforms are now being redirected at marine energy. Ecospace, an ecosystem model with fisheries roots, has been used to project cumulative impacts of combined aquaculture and marine energy platforms off Scotland, while spatiotemporal analyses have mapped offshore wind development scenarios across the North Sea basin. The review also flags an incoming wave of artificial intelligence: machine learning models already predict hydrodynamic and water quality responses relevant to energy siting, and large language models are being used to assemble ecosystem models far faster than manual parameterization allows. The authors predict AI and machine learning will make substantial contributions to cumulative assessment within a decade, while cautioning that models must still remain interpretable to the human decision-makers who rely on them.

Project-level practice, however, lags far behind the research frontier. Many consenting documents still amount to a cursory statement that no significant cumulative effects are anticipated, a formula the review’s authors describe as opaque and high-level. The critique echoes twenty years of published complaints: shifting baselines, single-industry focus, superficial expert assurances, and poor communication of uncertainty. Part of the fix, the authors argue, is structural. Nesting project-level assessments within strategic regional assessments, as maritime spatial planning frameworks increasingly attempt, would spread the analytical burden and ensure system-wide effects are not missed. A Baltic Sea case study, which embedded cumulative assessment at multiple scales into spatial planning and explicitly included marine energy among the activities considered, is held up as a template that balances rigor with pragmatism.

The recommendations that close the review are pointed. Assessments should use standardized terminology and terms of reference across jurisdictions; risk criteria should be set with stakeholders before analysis begins; predictive models should underpin assessments wherever possible; and every assumption, limitation, and uncertainty should be documented transparently, ideally communicated through IPCC-style confidence matrices. Centralized, interoperable data platforms, from the United States’ PRIMRE to Europe’s MARENDATA and France’s ResCORE, should be maintained and linked to reduce duplicated monitoring effort. Field studies must disentangle single-pressure from multi-pressure responses, and region-specific reference points defining acceptable levels of effect need to be developed, potentially borrowing methods from fisheries management. Crucially, the authors stress that assessments cannot remain a planning formality: they must be validated against post-deployment monitoring, shifting from hazard analysis to genuine evaluation of realized risk. For an industry still small enough to learn cheaply, the message is clear: borrow the hard-won methods of offshore wind, fisheries, and even terrestrial mining now, before the arrays grow too large for trial and error.

Subject of Research: Cumulative effects assessment methods for marine renewable energy development

Article Title: Marine renewable energy and cumulative effects assessments: Summary of the state of play

Article References: Fulton, E. A., Hemery, L. G., Copping, A. E., Farr, H. K., Fox, J., Garavelli, L., Hasselman, D. J., Miller, R., & O’Hagan, A. M. (2026). Marine renewable energy and cumulative effects assessments: Summary of the state of play. Environmental Challenges, 25, Article 101674. https://doi.org/10.1016/j.envc.2026.101674

Image Credits: AI Generated

DOI: 10.1016/j.envc.2026.101674

Keywords: marine renewable energy, cumulative effects assessment, environmental impact assessment, offshore energy, marine spatial planning, ecosystem modeling, tidal energy, wave energy, ocean stressors, Bayesian networks, marine ecosystems, regulatory policy

The motivation for the work stems from Taiwan’s uniquely exposed geography. Sitting on the western edge of the Pacific, the island lies in the path of tropical cyclones spawned between the South China Sea and the Philippines. According to Taiwan’s Central Weather Administration, roughly 26 typhoons form each year in the northwest Pacific, and an average of about three strike Taiwan annually, mostly between July and October. National scientific assessments cited in the study estimate that under the high-emission RCP8.5 scenario, the probability of severe typhoons affecting the island could reach 100 percent by the end of the twenty-first century. Meanwhile, official projections indicate that in a 2 degree Celsius warming world, the 50-year return-period storm surge change rate along Taiwanese coasts could rise by between 0.48 and 30.39 percent, and typhoon wave change rates by 7.8 to 17.29 percent.

Taitung City offers an almost laboratory-like setting for studying these dynamics. The collision of the Philippine Sea Plate and the Eurasian Plate has produced steep, convoluted bathymetry, meaning deep water lies close to shore and typhoon waves arrive with little terrain to attenuate them. Sediment transport along the roughly 13.5-kilometer study coastline runs predominantly north to south, fed largely by rivers, so beaches widen near river mouths and thin with distance from them. Disaster records document coastal damage during Typhoon Dujuan in 2003, Typhoon Matmo in 2013, Typhoon Soudelor in 2015 and Typhoon Mangkhut in 2018. Notably, Typhoon Mangkhut damaged the riprap of a coastal revetment in the Fengyuan sector even though it never made a direct landfall, underscoring how swell from distant storms can reshape this shoreline.

The methodological core of the study is the Bayesian network, a framework that fuses probability theory with graph theory. Variables are represented as nodes in a directed acyclic graph, and arrows between nodes encode conditional dependencies, capturing how the probability of one factor changes given the state of another. Prior probabilities describe the baseline likelihood of each risk grade, while conditional probabilities quantify the transitivity and causality that traditional risk matrices ignore. Because the network combines new evidence with prior distributions through Bayesian inference, it can be updated dynamically as monitoring data accumulate, and it yields full probability distributions rather than a single deterministic output. The authors argue this makes it far better suited than linear regression to random, nature-driven hazards and to the surprise events that deterministic models handle poorly.

Building the model required identifying the factors that genuinely drive risk along this coast. The team used anonymous Fuzzy Delphi questionnaires to extract representative hazard factors: geophysical properties, wave energy, shoreline change rate, the width of the buffer zone between the 0-meter and 2-meter isobaths, historical damage records and structural inspection results. Vulnerability was captured through population density, land use and the distribution of emergency shelters. Each factor was classified into five risk grades spanning low to high, and weights were assigned via the analytic hierarchy process. Crucially, the network’s arcs reflect physical causation rather than mere correlation. Wave energy is modeled as the mother node driving shoreline change rates, while a narrow buffer zone steepens wave dissipation and increases the load delivered to defenses and the shoreline behind them.

The empirical backbone came from twelve sets of bathymetric survey data collected over two decades, with the 0-meter isobath of November 2004 serving as the benchmark for shoreline change analysis. The coast was divided into 500-meter sections rather than defense units, a refinement that revealed how hazard potential varies along the shoreline. Sections S1 through S5, near the Beinan River estuary, showed pronounced seaward accumulation thanks to abundant river sediment, while sections S13 through S18, located at the end of the sediment transport unit, displayed persistent erosion potential, with the 0-meter isobath in some places already retreating to the foundations of coastal defenses. Incident wave heights for each typhoon event were simulated with the MIKE 21 SW spectral wave model, driven by recorded typhoon positions, central pressures and wind speeds.

Two subtle human interventions reshaped the sediment budget during the study period. Since 2014, authorities have tackled winter fugitive estuary dust from the bare Beinan River mouth by planting vegetation, spraying water and laying straw mats, and since 2011 by constructing water terrace engineering that keeps the estuary surface wet. These measures reduced dust hazards but also slowed river flow velocity, cutting the sediment delivered to the river mouth and reversing the shoreline change trend in the estuary sectors after 2014. The researchers note this as a textbook case of anthropogenic maladaptation: an engineering solution to one hazard quietly amplified another by starving the adjacent beaches of sand.

Validation of the network produced encouraging results. Trained on data from 2004 to 2020, the Bayesian module was asked to forecast the hazard risk grades of 23 coastal sections for 2021. Predictions matched observations in 17 sections outright, and the remaining six fell within an adjacent risk grade rather than diverging wildly, yielding an overall predictive accuracy of about 74 percent. The authors emphasize that slight overestimation of risk is a feature rather than a flaw for hazard management, since it keeps authorities alert. Perhaps more compelling, causality verification found a strong correlation, with a coefficient of determination of roughly 0.81, between wave energy and shoreline change rate during historical damage events, confirming that the causal structure encoded in the network mirrors real physical behavior under extreme conditions.

When the team fed the network boundary conditions for the 2 degree Celsius warming scenario, including a sea-level rise of 0.35 meters and increased design water levels and deep-sea wave heights, the projected hazard landscape shifted measurably. Wave energy grades generally increased or held steady, and incident wave heights rose in the estuary sectors of both the Beinan and Lijia rivers. The maximum probability of a high hazard grade for wave energy-propagated shoreline change climbed to 60 percent in the Beinan River estuary, a jump of 43 percentage points, while the sediment supply termination sector saw a maximum increase of about 8 percent. Crucially, the conditional probabilities revealed that shoreline change risk grades become more polarized under warming, concentrating in extremes of high and low rather than spreading across middle categories, and pinpointing the estuary and sediment-termination sectors as erosion hotspots demanding priority patrols and adaptation measures.

The study’s authors are candid about the limits of a model trained on twenty years of data with occasional gaps in bathymetric coverage, noting that its purpose is not instantaneous perfection but a framework that grows sharper as monitoring datasets expand. They recommend that authorities periodically update the conditional probabilities, and they suggest a concrete mitigation: relocating sediment from the bare Beinan River estuary to the sediment-starved sectors S3 through S5, restoring continuity of supply and easing climate-driven risk. For a densely populated planet where fifteen of Taiwan’s sixteen coastal counties have already faced erosion, the message resonates far beyond Taitung: probabilistic causal models can turn fragmented environmental monitoring into actionable foresight, telling coastal managers not just where the sea will bite, but how likely it is to bite hard.

Subject of Research: Probabilistic forecasting of coastal erosion and hazard risk using a Bayesian network along the Taitung coast of Taiwan under climate change

Article Title: Coastal risk assessment and hazard forecast analysis via a Bayesian network

Article References: Ye, C.-J., Huang, W.-P., & Hsu, J.-C. (2026). Coastal risk assessment and hazard forecast analysis via a Bayesian network. Natural Hazards, 122(19), Article 633. https://doi.org/10.1007/s11069-026-08335-y

Image Credits: AI Generated

DOI: 10.1007/s11069-026-08335-y

Keywords: Bayesian network, coastal erosion, hazard forecast, Taitung, Taiwan, climate change, shoreline change, wave energy, sediment supply, risk assessment, sea-level rise, Natural Hazards