Predicted Mass Loss Research Articles

Amundsen Sea Embayment ice-sheet mass-loss predictions to 2050 calibrated using observations of velocity and elevation change

Abstract Mass loss from the Amundsen Sea Embayment of the West Antarctic Ice Sheet is a major contributor to global sea-level rise (SLR) and has been increasing over recent decades. Predictions of future SLR are increasingly modelled using ensembles of simulations within which model parameters and external forcings are varied within credible ranges. Accurately reporting the uncertainty associated with these predictions is crucial in enabling effective planning for, and construction of defences against, rising sea levels. Calibrating model simulations against current observations of ice-sheet behaviour enables the uncertainty to be reduced. Here we calibrate an ensemble of BISICLES ice-sheet model simulations of ice loss from the Amundsen Sea Embayment using remotely sensed observations of surface elevation and ice speed. Each calibration type is shown to be capable of reducing the 90% credibility bounds of predicted contributions to SLR by 34 and 43% respectively.

A comparison of contemporaneous airborne altimetry and ice-thickness measurements of Antarctic ice shelves

AbstractEstimates of ice shelf mass loss are typically based on surface height measurements, assuming hydrostatic equilibrium and estimated firn thickness. Recent investigations, however, challenge the assumption that ice shelves are freely floating, particularly in proximity to narrow structures such as basal channels and shear margins. We compare contemporaneous measurements of Antarctic ice shelf thickness, from ice-penetrating radar, to freeboard height, from laser altimetry, acquired during multiple airborne surveys. On average, the hydrostatic thickness differs from observed thickness by at least ~17 ± 98 m, but this difference varies well beyond the propagated error within and among ice shelves, and depends on the corrections applied. We find that uncertainty in firn thickness can account for most, but not all, of the imbalance. Overall, errors in hydrostatic thickness do not significantly impact estimated basal melt rates. Our results indicate that localized approaches to estimating ice shelf thickness and rates of change are not applicable at large scales, and vice versa, and point to the need for more abundant and accurate firn and ice thickness measurements to improve estimates and predictions of ice shelf mass loss.

New predictions for radiation-driven, steady-state mass-loss and wind-momentum from hot, massive stars

Context. Massive stars lose a large fraction of their mass to radiation-driven winds throughout their entire life. These outflows impact both the life and death of these stars and their surroundings. Aims. Theoretical mass-loss rates of hot, massive stars are derived to be used in applications such as stellar evolution. The behaviour of these rates in the OB-star regime is analysed, and their effects on massive-star evolution predictions studied. Methods. Dynamically consistent models are computed by solving the spherically symmetric, steady-state equation-of-motion for a large grid of hot, massive stars with different metallicities. The radiative acceleration is derived from non-local thermodynamic equilibrium radiative transfer in the co-moving frame, and all models cover a large spatial range from deep subsonic atmospheric layers into the radiation-driven and highly supersonic wind outflow. The resulting mass-loss rates are used to derive a simple scaling recipe with stellar parameters (luminosity, mass, effective temperature, and metallicity), and the new recipe is used to evaluate some first impacts upon massive-star evolution tracks. Results. We provide a new prescription for steady-state, radiation-driven mass-loss from hot, massive stars depending on their fundamental parameters. In accordance with our previous work, the rates for O stars are lower a factor of ~3 than the rates typically used in previous stellar-evolution calculations, where differences generally decrease with increasing luminosity and temperature. For cooler B giants/supergiants we find larger discrepancies, of up to one or even two orders of magnitude. This arises because we do not find any systematic increase in mass-loss rates below the so-called bi-stability region; indeed, our results do not show any sign of a significant bi-stability jump within the parameter range covered by the grid (Teff ≥ 15 kK). Due to the lower mass-loss rates we find that massive-star envelopes are not easily stripped by means of standard steady-state winds, making it difficult to create classical Wolf-Rayet stars via this channel. Moreover, since the stars retain more mass right before they die as supernovae, our new rates make it possible to create black holes of higher masses than in previous models, even at Galactic metallicity. However, a remaining key uncertainty regarding these predictions concerns unsteady mass-loss for very high-luminosity stars close to the Eddington limit as well as the impact of non-line-driven winds.

A Transiting Super-Earth in the Radius Valley and an Outer Planet Candidate Around HD 307842

We report the confirmation of a TESS-discovered transiting super-Earth planet orbiting a mid-G star, HD 307842 (TOI-784). The planet has a period of 2.8 days, and the radial velocity (RV) measurements constrain the mass to be . We also report the discovery of an additional planet candidate on an outer orbit that is most likely nontransiting. The possible periods of the planet candidate are approximately 20–63 days, with the corresponding RV semiamplitudes expected to range from 3.2 to 5.4 m s−1 and minimum masses from 12.6 to 31.1 M ⊕. The radius of the transiting planet (planet b) is , which results in a mean density of suggesting that TOI-784 b is likely to be a rocky planet though it has a comparable radius to a sub-Neptune. We found TOI-784 b is located at the lower edge of the so-called “radius valley” in the radius versus insolation plane, which is consistent with the photoevaporation or core-powered mass-loss prediction. The TESS data did not reveal any significant transit signal of the planet candidate, and our analysis shows that the orbital inclinations of planet b and the planet candidate are and ≤88.°3–89.°2, respectively. More RV observations are needed to determine the period and mass of the second object, and search for additional planets in this system.

Durability analysis on properties of water soaked PNNCs and CS-ANN model for wear property analysis of PNNCs

The work aims to prepare and characterize polyester nanoclay nanocomposite (PNNCs) with various nanoclay weight percentages (0, 2, and 4). Nanoclay and polyester resin are blended using a mechanical stirrer followed by a sonicator. The blend is molded as specimens as per ASTM standards. The addition of nanoclay improved tensile strength by 12% to 16% and flexural strength by 4% to 10%. After 60 days of soaking in, the tensile strength retention rate of pure PE, 2PNNC, and 4PNNC are 84.9%, 89.8%, and 90.6%, respectively. At the same time, flexural strength retention rates of pure PE, 2PNNC, and 4PNNC are 86.7%, 90.2%, and 91.9%, respectively. SEM images are analyzed to know the reasons for specimen failure under tensile load. ExpDec1 (“One-phase exponential decay function with time constant parameter”) model is used on the experimental data to determine the composite’s durability. The experimental values and data produced by the ExpDec1 model are relatively close to one another. In all specimens, the error percentage of experimental and predicted values during 80 and 100 days of water soaking varies very little (less than 1%). The study proposes CS-ANN (Cuckoo Search-Artificial Neural Network) architecture to predict mass loss. Test results prove that the CS-ANN predicted values are much closer to the experimental results. Cuckoo Search Algorithm (CSA) is used along with the ANN model to optimize and fine-tune the hyperparameters according to the data. The loss curves substantially prove the proposed model to be the best fit for the experimental data.

A novel physics-informed neural networks approach (PINN-MT) to solve mass transfer in plant cells during drying

Predicting microscale mechanisms of plant-based food materials has been an enduring challenge due to the inherent complexity of involved physics and prohibitively-high computational costs. As an alternative to conventional methods of solving corresponding Partial Differential Equations (PDEs), Physics-Informed Neural Networks (PINNs) have recently received significant attention from the research community. This article investigates the feasibility of PINNs' Automatic Differentiation (AD) to solve mass transfer (PINN-MT) and effectively predict cellular-level mass loss and subsequent moisture variations during low-temperature drying. PINN-MT has been developed by incorporating the residuals of convective mass transfer equation, Fick's law of diffusion and relevant initial conditions into the loss function. The integration of Adaptive Activation (AA) and Transfer Learning (TL) leads to improved computational efficiency, prediction accuracy and consistency. To the best of the authors' knowledge, this study is the first of its kind to apply PINNs' AD to solve mass transfer for food-drying. The PDE-solving capacity of the proposed approach has been compared with that of the state-of-the-art Finite Element Analysis (FEA) techniques. Compared to benchmark FEA results, percentage errors for PINN-MT-based mass loss and moisture distribution predictions remain below 0.23% and 0.33%, respectively. In addition, TL enhances the solution-based prediction efficiency of PINN-MT by more than 80%. Further, a novel approach has been proposed to achieve time-efficient predictions by feeding mass transfer coefficient-hm as an independent input to the proposed predictive framework, which has the potential to tackle complex and highly non-linear physics-based models, where conventional computational approaches usually struggle.

The synergic approach between machine learning, chemometrics, and NIR hyperspectral imagery for a real-time, reliable, and accurate prediction of mass loss in cement samples

Alternative and non-destructive analytical methods that predict analyte concentration accurately and immediately in a specific matrix are becoming vital in the analytical chemistry domain. Here, a new innovative and rapid method of predicting mass loss of cement samples based on a combination of Machine Learning (ML) and the emerging technique called Hyperspectral Imaging (HSI) is presented. The method has proved its reliability and accuracy by providing a predictive ML model, with satisfactory best validation scores recorded using partial least squared regression, with a reported ratio of performance to inter-quartile distance and root mean squared error of 12,89 and 0.337, respectively. Moreover, the possibility of optimizing and boosting the performance of the method by optimizing the predictive model performance has been suggested. Therefore, a features selection approach was conducted to disqualify non-relevant wavelengths and stress only relevant ones in order to make them the only contributors to a final optimized model. The best selected features subset was composed of 28 wavelengths out of 121, found by applying genetic algorithm combined to partial least squares regression as a feature selection method, on spectra preprocessed consecutively by the first-order savitzky-golay derivative calculated with 7-point quadratic SG filter, and multiplicative scatter correction method. The overall results show the possibility of combining HSI and ML for fast monitoring of water content in cement samples.

Thermal stability of polymethacrylic based ELIUM® resin: Effect of comonomers, antioxidants and aluminum trihydrate filler

This paper presents the study of the thermal degradation of methyl methacrylate resin based materials known as ELIUM® resin. Samples under investigation were made from MMA based resin (ELIUM® V1), ELIUM® V1 with a dimethacrylate comonomer (ELIUM® V2), and ELIUM® V2 with a stabilizer package (ELIUM® V3). They were used for trying to discuss the degradation mechanisms. Blocks differing by their thickness made from ELIUM® formulation filled with aluminum TriHydrate used as flame retardant were also investigated in order to better match some industrial materials. Degradation was observed to be mainly driven by unzipping, consistent with the methacrylic nature of ELIUM® resin. Occurrence of oxidation is also discussed depending on materials formulation and temperature. A blend of antioxidants was inefficient for limiting mass loss. At 200 °C, both ATH and ELIUM® degrade, which contributes to overall mass loss. At temperatures below 180 °C, ATH does not seem to induce new degradation mechanisms but would increase oxygen diffusivity. Finally, a first simple kinetic model is proposed to predict mass loss in thick composite blocks aged under air.

Prediction of mass loss and heat release rates measured in cone calorimeter experiments performed on glass fiber reinforced nylon 66 blended with red phosphorus

This work demonstrates an approach to building a burning model capable of predicting combustion behavior of multicomponent flame retardant (FR) materials. Glass fiber (GF) reinforced nylon 66 (PA) blended with red phosphorus (RP) with different material compositions were selected for this work. Cone calorimeter experiments at a radiant heat flux of 50 kWm−2 were conducted to measure the mass loss rates (MLR) and heat release rates (HRR) of each formulation. It was found that with 6% red phosphorus (by weight), the peak MLR and peak HRR measured in cone calorimeter experiments were reduced significantly by 45% and 60% compared to the sample without red phosphorus, respectively. Milligram-scale flame calorimeter (MFC) was also used to measure the HRR and to calculate the effective heats of combustion of gasified products for each formulation. The previously established pyrolysis parameter sets coupled with a flame heat feedback model and with effective heats of combustion of pyrolyzates obtained based on MFC and cone calorimeter experiments, were implemented into a numerical framework, ThermaKin2Ds, to predict the burning behavior. Both the MLR and HRR of PA/GF25 (without red phosphorus) and PA/GF-RP1.5 (1.5 wt% red phosphorus) were well-captured by the model. The model overestimated the MLR and HRR profiles for PA/GF-RP6.0 (6 wt% red phosphorus). Additional simulations were performed to account for the thin char layer formed above the sample during the experiments. It was concluded that this char layer mainly acted as a thermal barrier and its role as a gas barrier was minor. The developed burning model with the inclusion of this char layer captured the burning dynamics for this blend. Note that the model enables the prediction of burning behavior of FR blends as a function of material composition and heating condition.

Incorporating Horizontal Density Variations Into Large‐Scale Modeling of Ice Masses

AbstractGravity‐driven flow of large ice masses such as the Antarctic Ice Sheet (AIS) depends on both the geometry and the mass density of the ice sheet. The vertical density profile can be approximated as pure ice overlain by a firn layer of varying thickness, and for the AIS the firn thickness is not uncommonly 10%–20% of the total thickness, leading to not insignificant variation in horizontal density. Nevertheless, in most vertically integrated ice‐flow models today, the density is assumed constant, sometimes with an adjustment in thickness to compensate. In this study, we explore the treatment of horizontal density variations (HDVs) within vertically integrated ice‐sheet models. We assess the relative merits and shortcomings of previously proposed approaches and provide new formulations for including HDVs. We use perturbation analysis to derive analytical solutions that describe the impact of density variations on ice flow for both grounded ice and floating ice shelves, which reveal significant qualitative differences between each of the proposed density formulations. We recommend always including the horizontal gradient of the density in the body‐force term of the momentum equation, and in the mass‐conservationequation. Furthermore, by modeling the transient evolution of a large sector of the West Antarctic Ice Sheet (WAIS), we find that doing so leads to about a 10% correction in the estimated change in volume above flotation over 40 years. We conclude that including HDVs in flow modeling of the AIS is important for accurate predictions of mass loss.

Dynamic correction on condensation time relaxation coefficient of Lee model based on mass conservation mechanism

Lee model is commonly accepted for modelling the mass transfer processes subject to both evaporation or condensation. However, the mass loss or mass excess always exist due to the mismatch of condensation and evaporation coefficients. Rare study focused on the proper ratio of them in closed systems. In present study, a dynamic correction model (named mass-conservation model) against to the condensation time relaxation coefficient is proposed, which can adjust the condensation coefficient in each time step considering the mass conservation between the initial state and the current state. Three typical closed structures are introduced for model validation in which severe mass loss and temperature prediction deviation are recognized. Result shows that the mass variation calculated by present dynamic correction model is less than 0.5% while the maximum mass loss with other models could reach 48.31%. Meanwhile, the prediction error of the three Cases calculated with the present model is the smallest, with the maximum decrement of 29.73% compared with other models. It is also realized that the insufficient heat release and the mass non-conservation are the key factor resulting in the temperature prediction deviation, which is well solved by present correction model on condensation time relaxation coefficient.

Effect of particle characteristics, kinetics and transport phenomena on the prediction of particle mass loss and products yields during biomass fast pyrolysis

A low computational cost model for the pyrolysis of biomass anisotropic particles was used to study the effect of thermal conductivity of the particle, CRECK kinetic scheme implementation, intraparticle secondary reactions, heats of reaction, and advection on particle conversion prediction. The model considers a shrinking anisotropic cylindrical particle with biomass pseudo-components, the appearance of liquid intermediates, intra-particle mass and heat transport and secondary reactions of volatiles. The model was validated against oak and birchwood single particle experiments. More accurate predictions for mass loss evolution are found when considering the change of the particle thermal conductivity anisotropy with conversion and the temperature dependency of the thermal conductivities of the biomass and the gas phase, compared to ignoring these phenomena. The CRECK biomass pyrolysis reaction scheme provides insights into the effect of biomass composition on the evolution of the liquid intermediate phase allowing future studies about the presence of heavy compounds in the bio-oil and other phenomena such as aerosol ejection. Secondary intraparticle vapor phase reactions showed a negligible effect in volatiles conversion while heterogeneous reactions slightly overestimate its conversion. Heats of reaction and product advection are important for accurate prediction of particle mass loss.

Design and evaluation of a prototype solar energy driven onion curing system using CFD modeling

In developing countries like Ethiopia, where the time of harvesting coincides with the dry season, farmers cure onion bulbs naturally on the field. Field curing generally takes longer than artificial curing and results in more losses and reduced quality of the bulbs due to increased risks for infestations and uncontrolled suboptimal drying conditions. Large-scale artificial curing systems are expensive and electrical energy supply is limited in rural areas. A CFD model was employed to design an alternative and sustainable onion curing system that can be deployed on the field in rural areas. The developed CFD model was validated by comparing the predicted air velocity, temperature, and mass loss to measured values on a prototype curing system operated on a field in Ethiopia. A good agreement between the model and experimental value was observed for the time profiles of temperature at different positions in the bulk of onions during curing, expressed by a root mean square error of 1.1°C in the temperature range from 28 to 47°C, 0.16 m s−1 in the velocity range from 0.1 to 2.5 m s−1, and 0.565% for the mass loss that ranged up to 6.35%. The developed model was used to assess the air velocity, temperature, and relative humidity distribution in order to get an insight into the uniformity of curing of onion bulbs using the develop alternative curing system. For all of the examined curing durations, the drying air temperature variation inside 80% of the porous medium was < 3°C. In the remaining 20% of the porous medium, a temperature variation of up to 6°C was observed. Thus, the newly designed and developed curing system was found to cure the onion bulbs uniformly. Moreover, its performance was evaluated experimentally and the onions were cured to a desirable level of curing for long-term storage within a total curing duration of 48 h. It is vital to consider bulb shrinkage, particularly in the neck, in order to further improve the model mass loss prediction.

Calibrated Mass Loss Predictions for the Greenland Ice Sheet

AbstractThe potential contribution of ice sheets remains the largest source of uncertainty in predicting sea‐level due to the limited predictive skill of numerical ice sheet models, yet effective planning necessitates that these predictions are credible and accompanied by a defensible assessment of uncertainty. While the use of large ensembles of simulations allows probabilistic assessments, there is no guarantee that these simulations are aligned with observations. Here, we present a probabilistic prediction of 21st century mass loss from the Greenland Ice Sheet calibrated with observations of surface speeds and mass change using a novel two‐stage surrogate‐based approach. Our results suggest a sea‐level contribution ranging from 4 to 30 cm at the year 2100, proviso the assumption that our chosen ice sheet model’s physics represent reality.

Supraglacial lake expansion, intensified lake drainage frequency, and first observation of coupled lake drainage, during 1985–2020 at Ryder Glacier, Northern Greenland

Along the Greenland Ice Sheet margin, supraglacial lakes store and redistribute ice sheet surface run off, and comprise an important potential hydrological link between the ice surface and the base, with ramifications for subglacial drainage systems and ice flow. As a consequence of increasing global mean surface air temperatures, these lakes have been predicted to expand further inland and to affect larger areas of the ice sheet. However, as contemporary dynamics of such supraglacial lake expansion are not well studied, any assessment of their future implications remains afflicted with uncertainty. Here, recent changes in supraglacial lake distribution and expansion, and in their drainage behavior and frequency, are presented for Ryder Glacier, Northern Greenland, as concluded from a remote sensing based analysis. The 35-year time span covered in the analysis allows for the detection of trends in lake processes and ice velocity, which otherwise were found to exhibit large inter-annual variability. It also reveals the first occurrence of a coupled lake drainage event in 2002. By linking supraglacial lake expansion, drainage modes, and drainage frequency to the efficiency of the subglacial drainage system and ice flow on seasonal and decadal timescales, a contribution is made to better understand the complexity of coupled glacio-hydrological processes, and to help reduce uncertainties in predictions of future mass loss from the Greenland Ice Sheet.

Ice mass loss sensitivity to the Antarctic ice sheet basal thermal state

Sea-level rise projections rely on accurate predictions of ice mass loss from Antarctica. Climate change promotes greater mass loss by destabilizing ice shelves and accelerating the discharge of upstream grounded ice. Mass loss is further exacerbated by mechanisms such as the Marine Ice Sheet Instability and the Marine Ice Cliff Instability. However, the effect of basal thermal state changes of grounded ice remains largely unexplored. Here, we use numerical ice sheet modeling to investigate how warmer basal temperatures could affect the Antarctic ice sheet mass balance. We find increased mass loss in response to idealized basal thawing experiments run over 100 years. Most notably, frozen-bed patches could be tenuously sustaining the current ice configuration in parts of George V, Adélie, Enderby, and Kemp Land regions of East Antarctica. With less than 5 degrees of basal warming, these frozen patches may begin to thaw, producing new loci of mass loss.

Reversal of ocean gyres near ice shelves in the Amundsen Sea caused by the interaction of sea ice and wind

Abstract. Floating ice shelves buttress the Antarctic Ice Sheet, which is losing mass rapidly mainly due to ocean-driven melting and the associated disruption to glacial dynamics. The local ocean circulation near ice shelves is therefore important for the prediction of future ice mass loss and related sea-level rise as it determines the water mass exchange, heat transport under the ice shelf and resultant melting. However, the dynamics controlling the near-coastal circulation are not fully understood. A cyclonic (i.e. clockwise) gyre circulation (27 km radius) in front of the Pine Island Ice Shelf has previously been identified in both numerical models and velocity observations. Mooring data further revealed a potential reversal of this gyre during an abnormally cold period. Here we present ship-based observations from 2019 to the west of Thwaites Ice Shelf, revealing another gyre (13 km radius) for the first time in this habitually ice-covered region, rotating in the opposite (anticyclonic, anticlockwise) direction to the gyre near Pine Island Ice Shelf, despite similar wind forcing. We use an idealised configuration of MITgcm, with idealised forcing based on ERA5 climatological wind fields and a range of idealised sea ice conditions typical for the region, to reproduce key features of the observed gyres near Pine Island Ice Shelf and Thwaites Ice Shelf. The model driven solely by wind forcing in the presence of ice can reproduce the horizontal structure and direction of both gyres. We show that the modelled gyre direction depends upon the spatial difference in the ocean surface stress, which can be affected by the applied wind stress curl filed, the percentage of wind stress transferred through the ice, and the angle between the wind direction and the sea ice edge. The presence of ice, either it is fast ice/ice shelves blocking the effect of wind or mobile sea ice enhancing the effect of wind, has the potential to reverse the gyre direction relative to ice-free conditions.

Comparison of solid particle erosion predictions using the dense discrete phase and discrete element models

A numerical procedure involving the dense discrete phase model (DDPM) is used to calculate solid particle erosion. DDPM works in two mechanisms. First, the discrete particles are treated as a pseudofluid, and the interaction among particles is evaluated by solving the governing equations of the pseudofluid. Second, the equivalent pressure of the pseudofluid is applied to a single particle to reflect the blocking effect of high-concentration particles. The numerical procedure is well verified by comparison with the experimental data picked from a direct impact test. In addition, the DDPM predictions are compared with the discrete element model (DEM) predictions in detail. Both methods show that the predicted mass loss caused by sand per unit mass decreases with an increase in sand concentration. DDPM indirectly considers the influence of particle interactions on solid particle erosion, and the predicted erosion contours are more uniform and smoother than the DEM-predicted contours.

Investigation on the pyrolysis process, products characteristics and BP neural network modelling of pine sawdust, cattle dung, kidney bean stalk and bamboo

To realize resource utilization of waste and alleviate associated environmental pollution, the pyrolysis behaviour of pine sawdust (PS), cattle dung (CD), kidney bean stalk (KS) and bamboo (BA) was investigated. The mass loss, gaseous product evolution and kinetic parameters of these four materials during pyrolysis were analysed via TG-MS. According to the TG-MS results, a back propagation (BP) neural network model was developed for the mass loss prediction of different biomass pyrolysis. More importantly, FTIR and SEM-EDX were used to analyse the characteristics of bio-oil and biochar to facilitate further utilization of these pyrolysis products. The results indicated that PS exhibited the highest mass loss (87.25%) during pyrolysis at 800 °C, and the higher D value (1.23E-05) indicated that PS was more easily decomposed than other materials. In terms of gaseous products, PS produced more H2, C2H6, C3H8 and CO2 than did the other materials during pyrolysis, while BA produced more CH4 and H2O. In addition, the content of phenols or aromatic compounds in PS bio-oil was the highest, and the surface pores in the obtained PS biochar were uniform and regular, which verified that PS achieved a higher utilization value than that of the other materials considered. Finally, the established BP neural network models realized a satisfactory mass loss prediction performance with increasing temperature.

Experimental and numerical investigation of drying rate impact on moisture loss, exchange coefficient and drying shrinkage of cement paste

Assessment of moisture content evolution and drying shrinkage is important for the long-term operation of large concrete structures. This paper evaluates the impact of size and boundary conditions in predicting mass loss and drying shrinkage of cement-based materials at ambient temperature. The authors conducted the study on ordinary portland cement paste. The size of specimens spanned from 1 mm to 36 mm. The experiments were performed in environmental scanning electron microscope (ESEM), Dynamic Vapor Sorption (DVS), climatic chamber (CC) and saturated salt solutions chambers. The mass loss, as well as drying shrinkage, have been recorded and simulated. The numerical simulations of experimental results show the drying and shrinkage models’ ability to consider size effects, which is essential when such models are validated on a thin specimen at a laboratory scale for prediction at the structural scale. The drying model is used to discuss the convection exchange coefficient between the material and the environment.For the surface exchange coefficient, both an analytical derivation and a numerical sensitivity study, especially regarding the size effect, are carried out. Then, the Biot-type dimensionless number called Surface Bulk Constant (SBC) is proposed to provide an improved understanding of the type of boundary conditions for moisture transport.