Model Sensitivity Studies Research Articles

Inter-laboratory reproduction and sensitivity study of a finite element model to quantify human femur failure load: Case of metastases

IntroductionMetastases increase the risk of fracture when affecting the femur. Consequently, clinicians need to know if the patient's femur can withstand the stress of daily activities. The current tools used in clinics are not sufficiently precise. A new method, the CT-scan-based finite element analysis, gives good predictive results. However, none of the existing models were tested for reproducibility. This is a critical issue to address in order to apply the technique on a large cohort around the world to help evaluate bone metastatic fracture risk in patients. The aim of this study is then to evaluate 1) the reproducibility 2) the transposition of the reproduced model to another dataset and 3) the global sensitivity of one of the most promising models of the literature (original model). MethodsThe model was reproduced based on the paper describing it and discussion with authors to avoid reproduction errors. The reproducibility was evaluated by comparing the results given in the original model by the original first team (Leuven, Belgium) and the reproduced model made by another team (Lyon, France) on the same dataset of CT-scans of ex vivo femurs. The transposition of the model was evaluated by comparing the results of the reproduced model on two different datasets. The global sensitivity analysis was done by using the Morris method and evaluates the influence of the density calibration coefficient, the segmentation, the orientations and the length of the femur. ResultsThe original and reproduced models are highly correlated (r2 = 0.95), even though the reproduced model gives systematically higher failure loads. When using the reproduced model on another dataset, predictions are less accurate (r2 with the experimental failure load decreases, errors increase). The global sensitivity analysis showed high influence of the density calibration coefficient (mean variation of failure load of 84 %) and non-negligible influence of the segmentation, orientation and length of the femur (mean variation of failure load between 7 and 10 %). ConclusionThis study showed that, although being validated, the reproduced model underperformed when using another dataset. The difference in performance depending on the dataset is commonly the cause of overfitting when creating the model. However, the dataset used in the original paper (Sas et al., 2020a) and the Leuven's dataset gave similar performance, which indicates a lesser probability for the overfitting cause. Also, the model is highly sensitive to density parameters and automation of measurement may minimize the uncertainty on failure load. An uncertainty propagation analysis would give the actual precision of such model and improve our understanding of its behavior and is part of future work.

Enhancing thermal comfort assessment: A sensitivity study of PMV-PPD and adaptive models in an Algerian reference hotel across different climate zones

Enhancing thermal comfort assessment: A sensitivity study of PMV-PPD and adaptive models in an Algerian reference hotel across different climate zones

Sensitivity study of impact risk model results to thermal radiation damage model for large objects

NASA's Probabilistic Asteroid Impact Risk (PAIR) model assesses the likelihood of potential damage for asteroid impact scenarios. Fast-running models are used to capture the effects of different hazards. This paper looks specifically at local ground damage hazards, including blast overpressure and thermal radiation damage, for large object impact scenarios. A sensitivity study is conducted to determine which parameters, and over what ranges, cause impact risks to become sensitive to thermal damage. Two additional thermal models with different approaches are used for comparison. The study determined the current thermal model is most sensitive to the luminous efficiency parameter that reflects the model's uncertainty in the amount of energy contributing to the thermal radiation damage. This sensitivity was most apparent for the highest severity damage levels. Comparisons of the three models showed that, in addition to sensitivities within the models, the impact risks are also sensitive to the choice of thermal model. The study results were applied to the 2023 Planetary Defense Conference hypothetical asteroid impact scenario (800 m diameter, 10.29 Gt energy), and parameter ranges of interest were determined. At the serious damage level, luminous efficiencies above 0.006 (0.6%) showed a small chance of thermal playing an important role, while luminous efficiencies above 0.0008 (0.08%) led to thermal playing a significant role at the unsurvivable damage severity level. Study results are used to identify key areas where additional model refinement and better knowledge of asteroid properties may be important for improving damage estimates.

Extension and Validation of the Turbomachinery Capabilities of SU2 Open Source Computational Fluid Dynamic Code

Abstract Computational Fluid Dynamic (CFD) tools have revolutionized the way to design engineering systems, but most established codes are proprietary and closed-source, making it difficult, if not impossible, to modify, debug, or add new features to the code. To provide a freely available open-source CFD code for turbomachinery aerodynamics and aeroelasticity, this paper enhances the turbomachinery capabilities of the open-source SU2 code and demonstrates its capabilities of single-passage steady simulation, full-annulus unsteady simulation, and aeroelasticity analysis in two high-speed compressors, namely NASA Stage 35 and TUDa-GLR-OpenStage, and a linear cascade SC1. For the single-passage steady simulation of NASA Stage 35, the SU2 results are validated against the measured data and verified against the commercial solver ansys cfx, and the performance characteristics results are in reasonably good agreement with each other. For the single-passage steady simulation of TUDa-GLR-OpenStage, grid and turbulence model sensitivity studies are performed and results are validated against the measured data, and SU2 can predict both the performance characteristics and the radial profiles with sufficient accuracy. For the full-annulus unsteady simulation of NASA Stage 35, it is demonstrated that SU2 can predict the propagation of inlet distortion equally well as ansys cfx. For the linear cascade, SU2 can predict the unsteady pressure and aerodynamic damping coefficient accurately. The presented results demonstrate the turbomachinery aerodynamics and aeroelasticity capabilities of SU2. The major modifications of SU2 made in this work will be shared with the code maintainer and the community in the future.

A Sensitivity Study of a Bayesian Inversion Model Used to Estimate Emissions of Synthetic Greenhouse Gases at the European Scale

To address and mitigate the environmental impacts of synthetic greenhouse gases it’s crucial to quantify their emissions to the atmosphere on different spatial scales. Atmospheric Inverse modelling is becoming a widely used method to provide observation-based estimates of greenhouse gas emissions with the potential to provide an independent verification tool for national emission inventories. A sensitivity study of the FLEXINVERT+ model for the optimisation of the spatial and temporal emissions of long-lived greenhouse gases at the regional-to-country scale is presented. A test compound HFC-134a, the most widely used refrigerant in mobile air conditioning systems, has been used to evaluate its European emissions in 2011 to be compared with a previous study. Sensitivity tests on driving factors like—observation selection criteria, prior data, background mixing ratios, and station selection—assessed the model’s performance in replicating measurements, reducing uncertainties, and estimating country-specific emissions. Across all experiments, good prior (0.5–0.8) and improved posterior (0.6–0.9) correlations were achieved, emphasizing the reduced sensitivity of the inversion setup to different a priori information and the determining role of observations in constraining the emissions.The posterior results were found to be very sensitive to background mixing ratios, with even slight increases in the baseline leading to significant decrease of emissions.

Моделирование эрозионного износа титанового сплава высокоскоростным потоком частиц

Introduction. Predicting solid particle erosion (SPE) in gaseous flow and managing its intensity is still a relevant problem in mechanical engineering. It requires the development of a general modeling methodology, which also depends upon many special cases studying various physical processes. Such studies should also include verification analysis, process parameters and model sensitivity studies. Mainly computational fluid dynamics and finite element analysis (and mesh-free methods such as smooth particle hydrodynamics or similar) are used to simulate the erosion process. Papers focused on CFD simulation of solid particle erosion of metal alloys are widely presented, but most of it is associated with relatively low or medium particle velocities (< 100–150 m/s) and is close to uniform diameter distribution. This paper presents a CFD study of Ti6Al4V titanium alloy SPE at relatively high particle velocities and sufficiently non-uniform unimodal particle diameter distribution. The paper also studies the turbulence model influence and particle shape effect which appears as a “shape factor” coefficient in the particle drag model. Methods. The heterogenous flow simulation was based on the Reynolds-averaged Navier-Stokes formulation, where the particles, according to Euler-Lagrange formulation, were simulated as mathematical points with corresponding properties. The influence of turbulence models, such as k-epsilon standard, RNG k-epsilon, and a relatively new Generalized equation k-omega (GEKO) model and its coefficients were also studied. Oka and DNV erosion models were also compared based on the general sample mass loss and more specific erosion intensity profile criterions. The simulation results were compared to the lab-scale experimental results. Results and discussion. It is shown that neither erosion intensity profile or sample mass loss do not depend upon the turbulence model choice or GEKO parameters variation. As expected, erosion is dependent on the erosion model and its coefficients. A notable influence of the shape factor is shown. As the drag coefficient increased due to the particle shape, the erosion intensity decreased and the erosive profile on the surface also changed due to the changing velocity and diameter distribution of the heterogenous flow. It is expected that such results would be useful not only for erosion prediction in all areas of mechanical engineering, but also for wear management in mechanical assemblies and shot peening / shot peen forming management and simulation.

Carbon dioxide exchange in an idealized valley

On a global scale the budget of carbon dioxide (CO2) bears a substantial uncertainty, which is commonly understood to be mainly due to land-surface exchange processes. In this paper it is examined to what extent mountainous model topography can amplify these land-surface exchange processes. The underlying hypothesis is that, on the meso-scale, topography adds additional atmospheric mechanisms that drive the exchange of CO2 at the surface.This model sensitivity study represents an advancement in model development through the implementation of a pioneering combination of the Weather Research and Forecasting (WRF) model, coupled with the Community Land Model (CLM4) to account for photosynthesis, and the semi-empirical TPGPP-LAI model to address ecosystem respiration. An idealized sine-shaped valley is investigated to examine the effect of complex topography on the CO2 budget compared to flat terrain. The systematic variation of meteorological initial conditions and plant functional types create an ensemble that unveils the fundamental factors which dominate the differences of CO2 fluxes between simulations with topography compared to a flat reference surface.During daytime, when there is photosynthesis, the vegetation temperature of the hypothetical sub-grid scale valley is on average 0.7 K colder and its vapor pressure deficit is 0.8 hPa lower which results in stronger net ecosystem exchange. During nighttime the valley is 0.6 K warmer than the plain, which increases ecosystem respiration compared to the plain. Over 24 h the valley is 0.1 K colder, but it acts as a sink of CO2 as the two opposing processes, during daytime and at night, result in 0.2 gC m−2 d−1 stronger net ecosystem exchange in the valley. During daytime differences are caused implicitly by meso-scale circulations through a secondary effect, as slope-winds shape the vertical temperature and humidity profiles, whereas at night the larger surface area of the valley is the prime cause for the differences. In contrast, the effect of differences in ambient CO2 on the ecosystem exchange is negligible.

The Development of METAL-WRF Regional Model for the Description of Dust Mineralogy in the Atmosphere

The mineralogical composition of airborne dust particles is an important but often neglected parameter for several physiochemical processes, such as atmospheric radiative transfer and ocean biochemistry. We present the development of the METAL-WRF module for the simulation of the composition of desert dust minerals in atmospheric aerosols. The new development is based on the GOCART-AFWA dust module of WRF-Chem. A new wet deposition scheme has been implemented in the dust module alongside the existing dry deposition scheme. The new model includes separate prognostic fields for nine (9) minerals: illite, kaolinite, smectite, calcite, quartz, feldspar, hematite, gypsum, and phosphorus, derived from the GMINER30 database and also iron derived from the FERRUM30 database. Two regional model sensitivity studies are presented for dust events that occurred in August and December 2017, which include a comparison of the model versus elemental dust composition measurements performed in the North Atlantic (at Izaña Observatory, Tenerife Island) and in the eastern Mediterranean (at Agia Marina Xyliatos station, Cyprus Island). The results indicate the important role of dust minerals, as dominant aerosols, for the greater region of North Africa, South Europe, the North Atlantic, and the Middle East, including the dry and wet depositions away from desert sources. Overall, METAL-WRF was found to be capable of reproducing the relative abundances of the different dust minerals in the atmosphere. In particular, the concentration of iron (Fe), which is an important element for ocean biochemistry and solar absorption, was modeled in good agreement with the corresponding measurements at Izaña Observatory (22% overestimation) and at Agia Marina Xyliatos site (4% overestimation). Further model developments, including the implementation of newer surface mineralogical datasets, e.g., from the NASA-EMIT satellite mission, can be implemented in the model to improve its accuracy.

Application of ADMS-Urban for an area with a high contribution of residential heating emissions - model verification and sensitivity study for PM2.5

Air pollution poses a significant risk to both human health and the environment in the contemporary world. Among the various pollutants, particulate matter with a diameter <2.5 μm (PM2.5) is regarded as the most hazardous. It has been implicated in over four million global fatalities in 2019 alone. This research paper divulges the outcomes of modelling the spatial-temporal fluctuations of PM2.5 concentrations within the confines of Wroclaw, a city situated in Poland, Central Europe. The model's output was evaluated through comparison with collected data from two government-operated monitoring stations within the city. For this study, we used the ADMS-Urban model and tested two different sources of background data (low-cost sensors and the EMEP MSC-W atmospheric chemistry transport model). The statistical analysis conducted in the paper indicates that the model reproduces the temporal variability of PM2.5. The conclusions from this research indicate that the average annual PM2.5 concentration within Wroclaw is 13.8 μg/m3, with the concentration peaking in the month of March. The spatial distribution reveals the highest PM2.5 concentrations primarily in the southern and western zones of the city, with additional elevated concentrations observed sporadically throughout the city. The study unveils that 1.3 % of Wroclaw's area experiences PM2.5 concentrations exceeding the EU's annual limit of 20 μg/m3. When considered in relation to the WHO's suggested annual average level of 5 μg/m3, Wroclaw city experiences exceedances throughout. When background concentrations are excluded from the model, the annual average PM2.5 concentration across the city is noted to be reduced by >50 %. A thorough investigation of the city's emission structure, taking into account only emissions from the city without background, indicates that the residential sector contributes about 77.3 % of the total annual average PM2.5 concentration in Wroclaw. The transportation and industrial sectors account for nearly 19.5 % and 3.2 %, respectively.

Atmospheric Response to Antarctic Sea-Ice Reductions Drives Ice Sheet Surface Mass Balance Increases

Abstract The mass balance of the Antarctic ice sheet is intricately linked to the state of the surrounding atmosphere and ocean. As a direct result, improving projections of future sea level change requires understanding change in the Antarctic atmosphere and Southern Ocean, and the processes that couple these systems. Here, we examine the influence of sea ice cover on the overlying atmosphere and subsequently the surface mass balance (SMB) of the adjacent Antarctic ice sheet. We investigate these processes both over the observational era using the ERA5 atmospheric reanalysis and in ensemble simulations of the Community Earth System Model 2.1 (CESM2) where only sea ice coverage is altered. Comparing extreme high and low sea ice over the satellite era in ERA5 reveals atmospheric and ice sheet SMB anomalies that largely mirror anomalies simulated by CESM2 in response to sea ice loss. Results highlight significant near-surface atmospheric warming in response to sea ice reductions that are particularly pronounced in nonsummer seasons and driven by significant ocean-to-atmosphere turbulent heat fluxes. In areas of sea ice loss, significant ocean surface evaporation increases occur. On the eastern flank of climatological low pressure systems, moisture is readily advected toward the ice sheet, driving positive anomalies in the ice sheet SMB. These results indicate that underestimation of Antarctic sea ice, which is common in many current-generation coupled climate models, may lead to overestimation of the ice sheet SMB and therefore underestimation of Antarctica’s contributions to global sea level. Significance Statement The Antarctic ice sheet is the largest potential source of global sea level rise. Its sea level contributions depend in part on how much snow accumulates across its surface. Through observation-incorporating reanalysis data and climate model sensitivity studies, we find that Southern Ocean sea ice coverage exerts an important influence on the near-surface climate and mass balance of the Antarctic ice sheet. Our results show that reductions in Antarctic sea ice promote enhanced ocean surface evaporation and subsequent increases in snowfall across the Antarctic ice sheet. Because current climate models tend to simulate too little Antarctic sea ice, we conclude they may therefore overestimate Antarctic ice sheet snowfall, leading to underprediction of future sea level rise.

Modeling forest canopy surface retrievals using very high-resolution spaceborne stereogrammetry: (II) optimizing acquisition configurations

The optimal configurations for very high-resolution (VHR, <2 m) spaceborne imagery collection to support stereogrammetry over complex forested terrain remain uncertain. We conducted a comprehensive sensitivity study of digital surface models (DSMs) derived from thousands of simulated along-track VHR stereopairs over two lidar-reconstructed forested scenes of closed and open canopies using the discrete anisotropic radiative transfer (DART) model. We evaluated the influence of convergence angle (CA), solar illumination, and image resolution on the derived DSM accuracy relative to the reference DSM and digital terrain model (DTM) products from airborne lidar data. Our results confirmed that the CA is the most critical acquisition parameter for DSM accuracy. Compared to the frequently used CA of ∼35∘ for along-track stereopair acquisitions by WorldView satellites, a smaller CA can provide better accuracy for forest canopy shape estimation by reducing occlusions and mitigating radiometric variance caused by the bidirectional reflectance characteristics of vegetation. For forested scenes over relatively flat terrain, oblique solar zenith angles (50−70∘) yielded more consistent DSMs with better accuracy, whereas images with a hotspot configuration generated elevations that were closer to the DTM. Image pairs with smaller ground sample distance (GSD) improved the DSM accuracy, and combinations of small (nadir) and large (off-nadir) GSDs had accuracy between those derived from homogeneous GSDs. These simulation results suggest that available global archives of DSMs from VHR stereo imagery collected under a range of acquisition configurations will yield inconsistent estimates of canopy surfaces. This study also provides a benchmark dataset and configuration guide for 1) selecting existing data to retrieve the forest canopy surface shape, and 2) defining requirements for future satellite missions to characterize the forest canopy surface using stereogrammetry.

A Sensitivity Study of a Thermal Propagation Model in an Automotive Battery Module

Thermal runaway is a major concern for lithium-ion batteries in electric vehicles. A manufacturing fault or unusual operating conditions may lead to this event. Starting from a single battery cell, more cells may be triggered into thermal runaway, and the battery pack may be destroyed. To prevent this from happening, safety solutions need to be evaluated. Physical testing is an effective, yet costly, method to assessing battery safety performance. As such, the potential of a numerical tool, which can cut costs and reduce product development times, is investigated in terms of capturing a battery module’s tolerance to a single cell failure. A 3D-FE model of a battery module was built, using a commercial software, to study thermal runaway propagation. The model assumes that when the cell jelly roll reaches a critical value, thermal runaway occurs. This approach was considered to study the module’s tolerance to a single cell failure, which was in reasonable agreement with what had been observed in full-scale experiments. In addition, quantitative sensitivity study on the i) model input parameters, ii) model space, and iii) time resolutions on the computed start time instant and time duration of thermal runaway were performed. The critical temperature was found to have the greatest influence on thermal runaway propagation. The specific heat capacity of jelly roll was found to significantly impact the thermal runaway time duration. The multi-physics model for battery thermal propagation is promising and worth to be applied with care for designing safer batteries in combination with physical testing.

Predicting transverse crack properties in continuously reinforced concrete pavement

Transverse crack properties (crack spacing and crack width) are important indicators of service life performance of continuously reinforced concrete pavements (CRCP). Crack properties are related to concrete material properties, reinforcement ratio, interface friction between both the concrete-base layer and concrete-steel, and the local climatic conditions. An improved analytical model with a bilinear concrete-base friction relationship is presented to predict crack spacing and crack width from concrete drying shrinkage and temperature contraction. The proposed 1D model was validated with test section data from a continuously reinforced concrete beam resting on asphalt base layer, which contained two reinforcement ratios, a standard paving concrete, and a lightweight aggregate concrete. A sensitivity study of model determined the bond stiffness coefficient between the concrete and reinforcement, bar spacing and diameter, elastic modulus of the reinforcement, and concrete drying shrinkage had the greatest impact on both crack spacing and crack width.

A Model Sensitivity Study of Ocean Surface Wave Modulation Induced by Internal Waves

AbstractNonlinear internal waves are an upper ocean phenomenon that drives horizontal surface current gradients, which in turn modulate ocean surface waves. Under certain conditions, this wave‐current interaction creates ocean surface roughness heterogeneity, in the form of alternating rough/smooth bands. In this study, we investigate the sensitivity of the modulation effect to internal wave properties and develop sea states using simulations of individual internal wave solitons. We utilize a phased‐resolved two‐layer fluid model to capture the evolution of surface waves deterministically. We conduct extensive simulations with a wide range of parameters, including fluid layer density ratio, internal wave amplitude, and parametric wind speed. We use the ratio of the mean surface slope between the rough and smooth bands, which are identified in the simulated surface wave field, to systematically investigate their response to the internal wave forcing across all our simulation cases. Our results show that, among the internal wave parameters, the upper‐lower layer density ratio causes the strongest surface heterogeneity. Spectral analysis of the surface wave elevation and slope variance reveals that the wavenumbers above the peak are most impacted. We demonstrate that accounting for the internal wave‐induced modulation requires including wave steepness statistics, for example, when modeling air‐sea exchange using a surface roughness, z0, parameter. Currently, these statistics are not included in typically coupled modeling schemes, and these systems cannot account for the impact of internal waves, even if the solitary wave phenomena are resolved.

Analytical Sensitivity Analysis of a Spent Nuclear Fuel Cask

Abstract Nuclear science and engineering is a field increasingly dominated by computational studies resulting from increasingly powerful computational tools. As a result, analytical studies, which previously pioneered nuclear engineering, are increasingly viewed as secondary or unnecessary. However, analytical solutions to reduced-fidelity models can provide important information concerning the underlying physics of a problem and aid in guiding computational studies. Similarly, there is increased interest in sensitivity analysis studies. These studies commonly use computational tools. However, providing a complementary sensitivity study of relevant analytical models can lead to a deeper analysis of a problem. This work provides the analytical sensitivity analysis of the one-dimensional (1D) cylindrical mono-energetic neutron diffusion equation using the forward sensitivity analysis procedure (FSAP) developed by Cacuci. Further, these results are applied to a reduced-fidelity model of a spent nuclear fuel cask, demonstrating how computational analysis might be improved with a complementary analytic sensitivity analysis.

Vertically migrating phytoplankton fuel high oceanic primary production

Marine net primary production (NPP) is remarkably high given the typical vertical separation of 50–150 m between the depth zones of light and nutrient sufficiency, respectively. Here we present evidence that many autotrophs bridge this gap through downward and upward migration, thereby facilitating biological nutrient pumping and high rates of oceanic NPP. Our model suggests that phytoplankton vertical migration (PVM) fuels up to 40% (>28 tg yr−1 N) of new production and directly contributes 25% of total oceanic NPP (herein estimated at 56 PgC yr−1). Confidence in these estimates is supported by good reproduction of seasonal, vertical and geographic variations in NPP. In contrast to common predictions, a sensitivity study of the PVM model indicates higher NPP under global warming when enhanced stratification reduces physical nutrient transport into the surface ocean. Our findings suggest that PVM is a key mechanism driving marine biogeochemistry and therefore requires consideration in global carbon budgets.

Global Dust Cycle and Direct Radiative Effect in E3SM Version 1: Impact of Increasing Model Resolution

AbstractQuantification of dust aerosols in Earth System Models (ESMs) has important implications for water cycle and biogeochemistry studies. This study examines the global life cycle and direct radiative effects (DREs) of dust in the U.S. Department of Energy's Energy Exascale Earth System Model version 1 (E3SMv1), and the impact of increasing model resolution both horizontally and vertically. The default 1° E3SMv1 captures the spatial and temporal variability in the observed dust aerosol optical depth (DAOD) reasonably well, but overpredicts dust absorption in the shortwave (SW). Simulations underestimate the dust vertical and long‐range transport, compared with the satellite dust extinction profiles. After updating dust refractive indices and correcting for a bias in partitioning size‐segregated emissions, both SW cooling and longwave (LW) warming of dust simulated by E3SMv1 are increased and agree better with other recent studies. The estimated net dust DRE of −0.42 Wm−2 represents a stronger cooling effect than the observationally based estimate −0.2 Wm−2 (−0.48 to +0.2), due to a smaller LW warming. Constrained by a global mean DAOD, model sensitivity studies of increasing horizontal and vertical resolution show strong influences on the simulated global dust burden and lifetime primarily through the change of dust dry deposition rate; there are also remarkable differences in simulated spatial distributions of DAOD, DRE, and deposition fluxes. Thus, constraining the global DAOD is insufficient for accurate representation of dust climate effects, especially in transitioning to higher‐ or variable‐resolution ESMs. Better observational constraints of dust vertical profiles, dry deposition, size, and LW properties are needed.

Evaluation of the Performance of the WRF Model in a Hyper-Arid Environment: A Sensitivity Study

Accurate simulation of boundary layer surface meteorological parameters is essential to achieve good forecasting of weather and atmospheric dispersion. This paper is devoted to a model sensitivity study over a coastal hyper-arid region in the western desert of the United Arab Emirates. This region hosts the Barakah Nuclear Power Plant (BNPP), making it vital to correctly simulate local weather conditions for emergency response in case of an accidental release. We conducted a series of high-resolution WRF model simulations using different combinations of physical schemes for the months January 2019 and June 2019. The simulated results were verified against in-situ meteorological surface measurements available offshore, nearshore, and inland at 12 stations. Several statistical metrics were calculated to rank the performance of the different simulations and a near-to-optimal set of physics options that enhance the performance of a WRF model over different locations in this region has been selected. Additionally, we found that the WRF model performed better in inland locations compared to offshore or nearshore locations, suggesting the important role of dynamical SSTs in mesoscale models. Moreover, morning periods were better simulated than evening ones. The impact of nudging towards station observations resulted in an overall reduction in model errors by 5–15%, which was more marked at offshore and nearshore locations. The sensitivity to grid cell resolution indicated that a spatial resolution of 1 km led to better performance compared to coarser spatial resolutions, highlighting the advantage of high-resolution simulations in which the mesoscale coastal circulation is better resolved.

Impact of Microstructure on Solar Radiation Transfer Within Sea Ice During Summer in the Arctic: A Model Sensitivity Study

The recent rapid changes in Arctic sea ice have occurred not only in ice thickness and extent, but also in the microstructure of ice. To understand the role of microstructure on partitioning of incident solar shortwave radiation within the ice and upper ocean, this study investigated the sensitivity of the optical properties of summer sea ice on ice microstructures such as the volume fraction, size, and vertical distribution of gas bubbles, brine pockets, and particulate matter (PM). The results show that gas bubbles are the predominant scatterers within sea ice. Their effects on the scattering coefficient and ice albedo are 5 and 20 times stronger respectively than the effect of brine pockets. Albedo and transmittance of ice decrease with higher concentration and larger size of PM particles. A 4-cm top layer of ice with high PM concentration (50 g/m3) results in a 10% increase in radiation absorption. The role of ice microstructure in the partitioning of radiation transfer is more important for seasonal than for multiyear ice, and more important for ponded than for snow-covered ice. Varying ice microstructure can obviously alter solar radiation transfer in the ice-ocean system, even with a constant ice thickness. Our results suggest that numerical models should take the variable microstructure of sea ice into account to improve model accuracy and to understand the interaction between internal variations in Arctic sea ice and the ocean, especially in summer.

Modelling the Scaling-Up of the Nickel Electroforming Process

Electroforming is increasingly gaining recognition as a promising and sustainable additive manufacturing process of the “Industry 4.0” era. Numerous important laboratory-scale studies try to shed light onto the pressing question as to which are the best industry approaches to be followed towards the process’s optimisation. One of the most common laboratory-scale apparatus to gather electrochemical data is the rotating disk electrode (RDE). However, for electroforming to be successfully optimised and efficiently applied in industry, systematic scale up studies need to be conducted. Nowadays, well-informed simulations can provide a much-desired insight into the novelties and limits of the process, and therefore, scaling up modelling studies are of essence. Targeted investigations on how the size and geometry of an electroforming reactor can affect the final product could lead to process optimisation through simple modifications of the setup itself, allowing immediate time- and cost-effective adjustments within existing production lines. This means that the accuracy of results that any scaled up model provides, if compared to a successful, smaller scale version of itself, needs to be investigated. In this work a 3-D electrodeposition model of an RDE was used to conduct geometry and model sensitivity studies using a commercial software as is often done in industry. As a next step, a 3-D model of an industrial-scale electroforming reactor, which was 90 times larger in electrolyte volume compared to the RDE, was developed to compare, and identify the key model parameters during scale up. The model results were validated against experimental data collected in the laboratory for both cases to assess model validity.