Transport Model Research Articles

Comparing PEMFC state-of-health characteristics obtained by Galvanostatic Charge Method with and without nitrogen flush

Onboard determination of state-of-health (SoH) parameters becomes increasingly important with tightened lifetime requirements for automotive polymer electrolyte membrane (PEM) fuel cell systems. One way to electrochemically characterize SoH parameters like active catalyst surface area (roughness factor, RF), double layer capacitance and membrane permeability is the nitrogen-flush-requiring Galvanostatic Charge Method (GCM). In this publication, a nitrogen-flush-free and thus, onboard-compatible adapted version of GCM (AGCM from Möller et al. (2023)), is directly compared to its original method, GCM. A 20-cell automotive shortstack was frequently characterized with both GCM and AGCM methods as well as standard Cyclic Voltammetry (CV) during a degradation test. Additionally, the evaluation model for AGCM was varied in two aspects: Firstly, modeling the hydrogen partial pressure difference (ΔpH2) responsible for hydrogen crossover and secondly, modeling hydrogen diffusion in the cathode volume with a 0-D transport model. The characterization results prove that all investigated model variations of AGCM can reproduce double layer capacitance results from GCM and CV, as well as the decreasing trend in roughness factor results. RF-values characterized by AGCM agree with either GCM or CV results, depending on the evaluation model. A thorough analysis revealed the neglected short circuit resistance in combination with the chosen modeling approach of ΔpH2 in AGCM as the reason. The two modeling approaches for ΔpH2 base on extreme assumptions about the homogeneity of hydrogen distribution in the anode volume. Neither of them could reproduce the membrane permeability of GCM. Instead, one overestimates, one underestimates it, indicating the truth to be in between the two extreme models. However, AGCM measurement conditions are identified as a lever to improve the AGCM permeability results and their robustness, even if the modeling approaches remain unchanged. Furthermore, this study showed that neglecting hydrogen diffusion in the cathode volume in the AGCM evaluation model leads to overestimation of the membrane permeability.

A Digital Twin of the Trondheim Fjord for Environmental Monitoring—A Pilot Case

Digital Twins of the Ocean (DTO) are a rapidly emerging topic that has attracted significant interest from scientists in recent years. The initiative, strongly driven by the EU, aims to create a digital replica of the ocean to better understand and manage marine environments. The Iliad project, funded under the EU Green Deal call, is developing a framework to support multiple interoperable DTO using a federated systems-of-systems approach across various fields of applications and ocean areas, called pilots. This paper presents the results of a Water Quality DTO pilot located in the Trondheim fjord in Norway. This paper details the building blocks of DTO, specific to this environmental monitoring pilot. A crucial aspect of any DTO is data, which can be sourced internally, externally, or through a hybrid approach utilizing both. To realistically twin ocean processes, the Water Quality pilot acquires data from both surface and benthic observatories, as well as from mobile sensor platforms for on-demand data collection. Data ingested into an InfluxDB are made available to users via an API or an interface for interacting with the DTO and setting up alerts or events to support ’what-if’ scenarios. Grafana, an interactive visualization application, is used to visualize and interact with not only time-series data but also more complex data such as video streams, maps, and embedded applications. An additional visualization approach leverages game technology based on Unity and Cesium, utilizing their advanced rendering capabilities and physical computations to integrate and dynamically render real-time data from the pilot and diverse sources. This paper includes two case studies that illustrate the use of particle sensors to detect microplastics and monitor algae blooms in the fjord. Numerical models for particle fate and transport, OpenDrift and DREAM, are used to forecast the evolution of these events, simulating the distribution of observed plankton and microplastics during the forecasting period.

Finite orbit width effects on turbulent transport of ion parallel momentum

Abstract A kinetic model for ion turbulent parallel momentum transport is developed with finite orbit width effects for Tokamak plasmas. It is shown that the curvature and gradient drifts of ions can introduce pressure perturbations into the transport equation of ion parallel momentum, which leads to a new source term. And the source term can be understood as a Coriolis force and can play a key role in the toroidal symmetry breaking during the spontaneous spin-up process.

Spatially resolved analysis of pancreatic cancer identifies therapy-associated remodeling of the tumor microenvironment.

In combination with cell-intrinsic properties, interactions in the tumor microenvironment modulate therapeutic response. We leveraged single-cell spatial transcriptomics to dissect the remodeling of multicellular neighborhoods and cell-cell interactions in human pancreatic cancer associated with neoadjuvant chemotherapy and radiotherapy. We developed spatially constrained optimal transport interaction analysis (SCOTIA), an optimal transport model with a cost function that includes both spatial distance and ligand-receptor gene expression. Our results uncovered a marked change in ligand-receptor interactions between cancer-associated fibroblasts and malignant cells in response to treatment, which was supported by orthogonal datasets, including an ex vivo tumoroid coculture system. We identified enrichment in interleukin-6 family signaling that functionally confers resistance to chemotherapy. Overall, this study demonstrates that characterization of the tumor microenvironment using single-cell spatial transcriptomics allows for the identification of molecular interactions that may play a role in the emergence of therapeutic resistance and offers a spatially based analysis framework that can be broadly applied to other contexts.

Hydraulically linked reservoirs simultaneously fed the 1975–1984 Krafla Fires eruptions: Insights from petrochemistry

The 1975–1984 Krafla Fires in northeast Iceland was the first plate-boundary rifting episode to be tracked using seismic and geodetic monitoring. Geophysical observations from this episode have inspired conceptual models of magma transport during plate spreading, but a lack of complementary petrologic insights has hindered a holistic understanding of the events. To address this knowledge gap, we studied the petrochemistry of all nine Krafla Fires basaltic eruptions. Our large dataset of new whole-rock, matrix glass and mineral analyses from samples collected during or shortly after each eruption reveal a clear compositional bimodality in the erupted magmas that persisted across the episode, with evolved quartz tholeiite (MgO = 5.7–6.4 wt.%) erupted inside Krafla caldera, and more primitive (usually olivine-normative) tholeiite (MgO = 6.4–8.7 wt%) erupted north of the caldera margin. Barometric calculations indicate tapping of these magmas from distinct reservoirs: a primitive lower-crustal reservoir at a most probable depth of ∼14–19 km, and a more evolved, shallower reservoir at a most probable depth of ∼7–9 km beneath the caldera. These reservoirs were tapped simultaneously in several of the nine eruptions, and in three events the two magma types mixed near the northern caldera margin. Varying levels of trace element depletion in the deep-sourced primitive melts reflect incomplete mixing of diverse mantle-derived melts at depth; the most enriched of these melts could be parental to evolved inside-caldera magma via fractional crystallization. Clinopyroxene rims on gabbroic nodules from primitive September 1984 lavas record lower crustal pressures, while diffusion models suggest that these rims grew up to within a few months before eruption. Ascent of the primitive magma from the lower crust thus occurred over timescales much shorter than eruptive repose periods, without prolonged stalling at shallow depths. These observations are inconsistent with the view that the eruptions were entirely fed by lateral magma outflow from the shallow reservoir. They instead require some decoupling of the flow paths of the two magma types: the primitive magma either bypassed the sub-caldera reservoir laterally or ascended vertically beneath the northern vents. The two reservoirs nonetheless shared a hydraulic connection and jointly responded to rifting. Comparison of the Krafla Fires with other rifting events and eruptions highlights the complexity and diversity of magma transport during plate boundary rifting events, which is not yet captured by a generalizable model. Integration of petrologic, geochemical and geophysical data is essential to provide a holistic view of future rifting events in Iceland and at other spreading centres.

From flood to turbidity current: combined models to simulate continent to ocean sediment transport in the Var system, France

Turbidity currents are a form of gravity-driven flow that occurs in subaqueous environments. These currents contain a low volumetric percentage of particles but are very important in transporting them from coastal to deep-marine environments. In the Var sediment routing system, southeastern France, there is a sedimentary record of turbidity currents formed by submarine landslides and flooding of the Var River. In 2006, there was continuous monitoring of the marine section that recorded four turbidity currents. To explore the connectivity between the terrestrial source and marine basin we linked two reduced complexity models of sediment transport and landscape change. The aim of the paper is to combine two models, one dedicated to the prediction of river water and sediment run-off, and the second dedicated to the transport, erosion, and sedimentation by a turbidity current. The landscape evolution model CAESAR-Lisflood was used to model discharge and sediment yield. The behaviour of turbidity currents was modelled with the cellular automata model CATS. From calibration of the two models against observations of discharge and suspended sediment in the Var River system, we find that in 2006, two rainfall events would have led to hyperpycnal turbidity currents. These two events match well with two of the four-recorded events. Focusing on the largest event we find that the source pulse of sediment from the terrestrial environment is short-lived, less than half a day, but contains a significant quantity of fine particles. The event transferred on the order of 105 m3 of suspended sediment from source to sink. This study demonstrates that hyperpycnal turbidity currents can be generated with concentrations as low as 2-6 kg/m3 at the Var River mouth far below the theoretical 40 kg/m3 threshold, suggesting active convective sedimentation in the surface plume and sea-water dilution at the flooding river mouth. A substantial amount of sediment (35% of the input volume) is directly transferred towards the deepest part of the system during a short-living hyperpycnal turbidity current. However, a considerable (65%) part remains in the surface as a hypopycnal plume feeding the hemipelagic sedimentation.

Deep learning-based source term estimation of hydrogen leakages from a hydrogen fueled gas turbine

Hydrogen fueled gas turbine is an efficient and environmentally friendly energy conversion equipment. However, it is prone to gas leakages from corrosion-prone components and pipe connections. In order to address gas leakage problems, source term estimation (STE) can be applied to estimate the location and intensity of the leakage sources. Traditional STE methods are based on an optimization procedure using an atmospheric transport and dispersion model, which requires considerable computation efforts and cannot meet the need of real-time implementation. The complex flow field around the gas turbine, the possible existence of multiple leakage sources, and the high-dimensional time series data further increase the difficulty of the STE problem. To address these challenges, in the present study, a deep learning based STE approach is proposed. The basic idea is to use long short-term memory auto-encoder (LSTM-AE) network to extract the encoded features of multi-sensor data and then use a deep neural network (NN) to establish the relationship between the encoded features and the source term parameters of hydrogen leakages. The multi-sensor data are generated using computational fluid dynamics (CFD) analysis to simulate relevant hydrogen leakage scenarios of concern. The results show that compared with other machine learning algorithms, the proposed approach has an overall best performance in terms of the STE accuracy. Specifically, its hydrogen leakage source localization accuracy is 0.9798, and the leakage strength estimation R-squared (R2) can reach 0.9632. When the training data proportion is only 20–30%, the proposed approach can still perform well, indicating the ability of the model to effectively predict the location and strength of the leakage source even with relatively less training data.

Dispersion in a slit with crossflow filtration through a porous wall

Crossflow filtration is a separation technique where fluid flows tangentially across a membrane or porous media, reducing clogging by sweeping away retained particles and allowing continuous filtration. Dispersion of solute matter in crossflow filtration plays an essential role in the separation performance of many industrial processes. The current work aims to generalize the Taylor dispersion theory and study the solute transport in a slit–porous medium system with a crossflow. The solute is depleted by the porous medium at the slit top porous wall or the interface between the slit and the porous medium, where the continuity of the solute concentration and mass flux is applied. The solute is simultaneously transported axially by the main flow along the slit and vertically by the crossflow perpendicular to the slit. Using the Reynolds decomposition and cross-sectional averaging techniques, the generalized reduced-order model for the advection-dispersion solute transport in the slit–porous medium system with the presence of a crossflow is established, where the effective velocity of the main flow and the effective dispersion coefficient are obtained. The analysis of the results reveals that the nondimensional Taylor dispersion coefficient scales with the Peclet numbers for the crossflow and the main flow, respectively, as DT∼Pev−5/3 (when Pev≥20) and DT∼Peu2. The proposed theoretical model, along with the findings of this study, paves the way for the fundamental study of dispersion in more complex systems.

Finite difference solution for contaminant transport through a GCL composite cutoff wall-aquifer system considering semipermeable membrane effect

Semipermeable membrane effect is an important factor affecting the contaminant transport behavior through the bentonite-based barriers for waste containment. However, this effect has been ignored in majority of the existing analytical and numerical studies on contaminant transport, although it has been widely demonstrated in laboratory and field tests. Taking into account the semipermeable membrane effect, this study presents a numerical model for one-dimensional contaminant transport through a geosynthetic clay liner (GCL) composite cutoff wall-aquifer system consisting of a bentonite-based wall, a GCL and an aquifer. The finite difference method is used to obtain the solution. The numerical solution is validated against the laboratory experimental data, the existing analytical solution and COMSOL software simulations. A series of parametric analyses are conducted using the proposed numerical solution. The results indicate that the semipermeable membrane effect has a significant impact on the contaminant transport behavior and ignoring this effect can greatly underestimate the contaminant breakthrough time and overestimate contaminant outflow. For the conditions examined in this paper, the inclusion of semipermeable membrane effect results in a 7.1 %∼78.6 % increase in breakthrough time and a 5.3 %∼40.7 % decrease in mass flux, and the semipermeable membrane effect in bentonite-based wall, relative to that in the GCL, has greater impact on contaminant transport. The concentration-dependent semipermeable membrane efficiency coefficient ω more significantly slows down the contaminant transport relative to the constant ω, particularly under the conditions of low hydraulic conductivity, low source concentration and low hydraulic gradient. Moreover, it’s important to avoid simultaneous deterioration of both the bentonite-based wall and the GCL, otherwise the breakthrough time of the GCL composite cutoff wall can decrease significantly.

Effects of grid resolution on regional modelled groundwater salinity and salt fluxes to surface water

Coastal fresh groundwaters face growing threats from rising withdrawals and climate change, with salinization of surface and groundwater as notable impacts. Recent developments in parallel variable-density modelling enable studying these threats at unexplored resolutions and extents. To improve the understanding of ground- and surface water salinization processes we designed an experiment to measure how spatial resolution affects both groundwater salinity distribution and salt loads to surface waters. An existing parallelized coupled variable-density groundwater flow and salt transport (VD-GWT) model is applied to a region in the Netherlands whereby the surface water drainage network is parameterized at grid resolutions between 10 and 250 m. The results show a strong dependency between resolution and the salinity distribution in shallow groundwater while simulated salt loads are affected by imperfect flux scaling. The computational resources needed for this test suggest that regional high-resolution VD-GWT models are feasible using HPC environments, albeit not practical.

Forward and inverse river contaminant transport modeling using group preserving scheme

Environmental concerns have necessitated the development of computational models predicting pollutant dispersion in natural water systems. Due to the ill-posed nature of the inverse contaminant transport equation, solving this equation using all stable and convergent inverse methods is impossible. Factors such as river geometry, unsteady and non-uniform flow, and tidal influences add to the complexity of the inverse problem. These factors have prompted the evaluation of Group Preserving Scheme for environmental applications. The inverse solution method derives a general equation for solving ordinary differential equations by addressing a dynamical system at negative time steps, ensuring convergence. Three test cases have been presented to evaluate Backward Group Preserving Scheme (BGPS). These have included validation using observational data from Missouri River, inverse simulation in a tidal river, and sensitivity analysis of parameters such as pollutant patterns, advection, dispersion, and decay coefficients. The dataset includes calibrated data from Missouri River, which demonstrated high accuracy, with Mean Relative Error (MRE) ranging from 2.8% to 5.0% for the inverse model. Under tidal conditions, accuracy decreases over time but remains robust, with a Nash–Sutcliffe efficiency of 0.77–0.96 and an MRE of 0.9%–5.9%. Sensitivity analysis revealed optimal model performance for Péclet numbers greater than 500. The model performed best with gradual, wide-peaked pollutant patterns and moderate decay rates (Damköhler number between 5 and 10). BGPS proves effective for transport simulation and concentration history reconstruction in complex rivers, including those with tidal influences, offering a robust tool for water contamination analysis in various flow conditions.

Machine Learning-Enabled performance exploration to unveil the potential of AuCuSe4 in thermophotovoltaic cell

In this work, AuCuSe4 direct bandgap (Eg = 0.52 eV) semiconductor has been examined as a thermophotovoltaic (TPV) material. Initially, the electronic structure of AuCuSe4 compound has been investigated using first-principles calculations. The study reveals a semiconducting nature of AuCuSe4 with a narrow bandgap computed by DFT-MBJ (Density Functional Theory with Modified Becke-Johnson) method which is lower than the experimental value. The optical properties show significant aniso-tropic by AuCuSe4 absorption in the solar spectrum, favoring TPV growth orientation. Subsequently, a single-junction AuCuSe4 TPV cell has been presented employing a device transport model with an n-p structure, operating at the black body and cell temperatures of 1538 K and 300 K, respectively. Notably, machine learning (ML) techniques are utilized to investigate the significance of each parameter in the model, enhancing the understanding of the system’s behavior and design optimization. Through optimization, the performance of the TPV cell has been enhanced, resulting in an efficiency of 11.56 %, accompanied by the current at short circuit, JSC=9.22 A/cm2, voltage at the open circuit, VOC=0.49 V, and Fill factor, FF=80 %. These findings suggest that AuCuSe4 holds promise as a suitable material for creating high-performing TPV cells.

Calculations of the conversion factor from organic carbon to organic matter for aerosol mass balance.

Aerosol mass balance studies based on filter samples require a conversion factor to derive organic matter (OM) concentrations from organic carbon (OC) measurements from thermo-optical methods. This factor provides indirect insights on the molecular structure of OM needed in chemical transport models. Site- and season-specific ratios of OC to OM (fOM:OC) were calculated using data from five rural background sites in France between 2012 and 2021 by relating the unidentified chemical fraction in PM2.5 samples to thermo-optical OC concentrations. Further, multiple linear formulations were used to evaluate the impact of possible artefacts on the determination of fOM:OC. The resulting fOM:OC was then compared to other estimates derived from online aerosol mass spectrometry data, showing good agreement. The spatial and temporal variability in fOM:OC is discussed considering factors such as seasonality, meteorological conditions and the atmospheric oxidative potential. Linear-mixed effect models were formulated to quantitatively determine the drivers which influence the fOM:OC at the French rural background sites. Both ozone and relative humidity were variables with statistically significant effects on fOM:OC, indicating that differences in the contributions from both photooxidation and water content, explain the variability in fOM:OC observed at the French rural background sites. Site-specific fOM:OC yielded more accurate PM2.5 mass closure and are therefore recommended in mass-balance exercises. Accurate fOM:OC are critical to maintain consistency in OM time series, especially in cases where filter-based time series may be replaced by state-of-the-art online instrumentation.

Quantifying the Contribution of Multiple Processes to the Dust Decreasing Trend in the Guliya Ice Core Over the Past 50 Years

AbstractDust records extracted from ice cores can facilitate the reconstruction of historical atmospheric dust levels and climate change. However, interpreting dust variations in ice cores is intricate because of the compounded influence of emission, transport, and deposition processes. This study investigated dust records retrieved from the Guliya ice cap drilled in 2015 on the West Tibetan Plateau using a mean trajectory transport and deposition model. Results showed that the Guliya dust concentration has exhibited a declining trend since the 1960s (−751 μg kg−1 yr−1). Applying an attribution approach, we discovered that low dust emission (80.3%) was the main cause of the drop in dust concentration, with changes related to transportation (5.2%) and deposition (14.5%) making only minor contributions. The weakening of surface wind speed in the desert and increasing precipitation in both the desert and glacier were the primary factors driving the decrease in Guliya dust concentration.

Self-diffusion is temperature independent on active membranes.

Molecular transport maintains cellular structures and functions. For example, lipid and protein diffusion sculpts the dynamic shapes and structures on the cell membrane that perform essential cellular functions, such as cell signaling. Temperature variations in thermal equilibrium rapidly change molecular transport properties. The coefficient of lipid self-diffusion increases exponentially with temperature in thermal equilibrium, for example. Hence, maintaining cellular homeostasis through molecular transport is hard in thermal equilibrium in the noisy cellular environment, where temperatures can fluctuate widely due to local heat generation. In this paper, using both molecular and lattice-based modeling of membrane transport, we show that the presence of active transport originating from the cell's cytoskeleton can make the self-diffusion of the molecules on the membrane robust to temperature fluctuations. The resultant temperature-independence of self-diffusion keeps the precision of cellular signaling invariant over a broad range of ambient temperatures, allowing cells to make robust decisions. We have also found that the Kawasaki algorithm, the widely used model of lipid transport on lattices, predicts incorrect temperature dependence of lipid self-diffusion in equilibrium. We propose a new algorithm that correctly captures the equilibrium properties of lipid self-diffusion and reproduces experimental observations.

Modeling and Control of Strip Transport in Metal Peeling

Modeling and Control of Strip Transport in Metal Peeling

Modeling and simulation of simultaneous transport and incompressible flows on all level sets in a bulk domain

Modeling and simulation of simultaneous transport and incompressible flows on all level sets in a bulk domain

Development of novel osteochondral scaffolds and related in vitro environment with the aid of chemical engineering principles

In tissue engineering, collaboration among experts from different fields is needed to design appropriate cell scaffolds and the required three-dimensional environment. Osteochondral tissue engineering is particularly challenging due to the need to provide scaffolds that imitate structural and compositional differences between two neighboring tissues, articular cartilage and bone, and the required complex biophysical environments for cultivating such scaffolds. This work focuses on two key objectives: first, to develop bilayered osteochondral scaffolds based on gellan gum and bioactive glass and, second, to create a biomimetic environment for scaffold characterization by designing and utilizing novel dual-medium cultivation bioreactor chambers. Basic chemical engineering principles were utilized to help achieve both aims. First, a simple heat transport model based on one-dimensional conduction was applied as a guideline for bilayer scaffold preparation, leading to the formation of a gelatinous upper part and a macroporous lower part with a thin, well-integrated interfacial zone. Second, a novel cultivation chamber was developed to be used in a dynamic compression bioreactor to provide possibilities for flow of two different media, such as chondrogenic and osteogenic. These chambers were utilized for characterization of the novel scaffolds with regard to bioactivity and stability under dynamic compression and fluid perfusion over 14 d, while flow distribution under different conditions was analyzed by a tracer method and residence time distribution analysis.

Observed APO Seasonal Cycle in the Pacific: Estimation of Autumn O2 Oceanic Emissions

AbstractIn this work, we investigated the seasonal cycle of atmospheric potential oxygen (APO), a unique tracer of air‐sea gas exchanges of molecular oxygen (O2) and carbon dioxide (CO2), expressed as APO = O2 + 1.1 × CO2. APO data were obtained from flask air samples collected since the late 1990s at three Japanese ground stations and on commercial cargo ships sailing between Japan and Australia/New Zealand, North America, and Southeast Asia. We also analyzed the APO spatial distribution and seasonal cycles with simulations from an atmospheric transport model using climatological oceanic O2 fluxes from an empirical product that relate O2 flux to ocean heat as input. Model simulations reproduced the observed APO seasonal cycles generally well, but with larger amplitudes and earlier occurrence of seasonal minima and maxima than in the observations. Moreover, the observed seasonal cycles exhibited larger APO enhancements than the simulations in autumn and early winter, especially in the North Pacific at 20°N–60°N. These enhancements remained when refining the comparison by adjusting the simulated APO peak‐to‐peak amplitudes and seasonal phases to the observations. This suggests additional O2 emissions in the North Pacific, not well expressed in the air‐sea O2 fluxes used as input for our model simulations. The average autumn enhancement at 40°N–60°N was approximately twice that measured at 20°N–40°N. Confirming previous studies, our results indicate two distinct mechanisms possibly contributing to the additional oceanic O2 emissions: outgassing from a subsurface shallow oxygen maximum at 20°N–40°N and autumn phytoplankton bloom at 40°N–60°N.

Shifting sands: The influence of coral reefs on shoreline erosion from short-term storm protection to long-term disequilibrium

Climate change is exacerbating shoreline erosion and flooding, posing significant risks to coastal communities. Although traditional coastal defenses such as seawalls, dykes, and breakwaters offer protection from these hazards, their high environmental and economic costs are driving interest in cost-competitive nature-based solutions. Coral reef restoration is a nature-based solution that may be particularly apt to mitigate tropical coastal flooding and shoreline erosion while providing benefits to local tourism, fisheries, and nature. However, the novelty of this field requires studies demonstrating the benefits of reefs for coastal protection. While the flood protection benefits of reefs have been well-documented, their effects on shoreline erosion are comparatively less understood. Here, we investigate the effects of coral reefs on shoreline erosion by comparing tropical beach responses at short and long timescales, as well as identifying important reef structural features influencing coastal erosion rates. Our analyses leveraged two key datasets created in this study: the first derived from a literature review on short-term shoreline erosion due to storm events, and another compiling >80 years of long-term erosion rates, bathymetry, habitat, and wave energy for the Hawaiian Islands of Kauaʻi, Oʻahu, and Maui. Our analyses reveal three key findings regarding the effects of reefs on shoreline erosion. Firstly, we find evidence for the role of reefs in mitigating shoreline erosion during storm events, with coral reef-protected beaches experiencing 97 % less beach volume loss than unprotected beaches. Secondly, a linear regression analysis demonstrates that coral reef structure and wave energy are important predictors of long-term shoreline erosion rates, explaining 34 % of the variation across the Hawaiian Islands. Consistent with prior research, we find beaches protected by coral reefs with shallow reef crests, wide reef flats, calmer offshore conditions, and positioned farther from the shore exhibit lower erosion rates than others. Finally, when comparing historical erosion rates of protected and unprotected beaches in Hawai'i, we find a seemingly incongruous pattern where coral reef-protected beaches eroded up to 2x faster than beaches without reefs. While the cause of the enhanced erosion is yet to be fully understood, a combination of coral reef structural degradation and sea-level rise is likely shifting the equilibrium profiles of reef-protected beaches inshore. These results emphasize the role of coral reefs in reducing coastal erosion during storm events while revealing contrasting erosion patterns over long timescales. Future studies would ideally broaden the scope to include various regions, utilize advanced sediment transport models, and undertake field experiments to deepen our understanding of coral reef-coupled shoreline dynamics.