Shape Model Research Articles

A New Fusion Feature Selection Model (FFSM) based Feature Extraction System for Hand Gesture Recognition

The research presented here proposes a unique approach for exact feature extraction that makes use of the Fusion Feature Selection Model (FFSM). The method encompasses various stages to preprocess and extract crucial features from first-person hand action (FHPA) images. Preprocessing involves video-to-frame conversion, RGB to grayscale conversion, an improved median filter, and Gaussian blur-based image smoothing. Segmentation is achieved using the Improved SwinNet to identify meaningful regions within the images. Feature extraction employs the Gabor Line Derivative (GLD) method, Active Shape Model (ASM), and Histogram of Oriented Gradients (HOG) to capture texture, edge, and shape information, respectively. Extensive experimental evaluations demonstrate the effectiveness of our proposed approach, achieving remarkable performance in accurate feature extraction tasks.

Bayesian Approach to Estimate Distribution of Relaxation Times for Lithium-Ion Batteries

Electrochemical impedance spectroscopy (EIS) has emerged as a powerful tool for analyzing the state of lithium-ion batteries due to its ability to provide rich electrochemical information. Traditionally, equivalent circuit and physical models have been utilized for interpreting EIS data. However, these methods possess inherent drawbacks: equivalent circuit models are non-unique, while physical models can be challenging to implement. As an alternative, the distribution of relaxation times (DRT) has garnered increasing attention in recent years. The DRT method enables the analysis of EIS data without predefining a model's shape, allowing for the decomposition of internal characteristics in complex systems, such as lithium-ion batteries. Despite its advantages, DRT estimation is an ill-posed inverse problem that often requires regularization or fine-tuning based on empirical choices, which can lead to the misinterpretation of EIS data.To address these challenges, we propose a Bayesian approach that enables the estimation of DRT while considering uncertainties, even in the presence of observational errors in EIS data. Moreover, the proposed method can assist in estimating the state of health (SOH) of the battery, which provides valuable insights into the battery's overall performance, longevity, and safety. To validate the efficacy of our proposed method, we apply it to synthetic EIS data and compare its performance with existing DRT estimation methods. The results show that the proposed Bayesian approach offers a more robust and accurate estimation of DRT. We also demonstrate the practical applicability of the proposed method by applying it to real lithium-ion battery EIS experimental data, where it successfully extracts meaningful information about the battery's internal state and SOH. In summary, the proposed DRT-based Bayesian method has the potential to enable a robust diagnosis of the internal state and SOH of lithium-ion batteries through EIS measurements. By incorporating this approach, researchers and industry practitioners can gain a deeper understanding of battery performance, facilitating the development of improved diagnostic techniques and battery management strategies to enhance the performance and reliability of lithium-ion batteries. This, in turn, could contribute to the broader adoption of sustainable energy solutions, such as electric vehicles and renewable energy storage systems.This work was supported by the Korea Institute of Energy Technology Evaluation and Planning (KETEP) grants funded by the Ministry of Trade, Industry & Energy, Republic of Korea (No. 20214910100070). Figure 1

Hybrid traffic scheduling in time‐sensitive networking for the support of automotive applications

AbstractTime‐sensitive networking (TSN) is considered one of the most promising solutions to address real‐time scheduling in in‐vehicle network due to its capabilities for providing deterministic service. The TSN working group proposed various traffic shaping mechanisms, while deterministic scheduling of hybrid traffic is still not effectively solved since the traffic requirements are difficult to satisfy by standalone or combined mechanisms with fixed time slot divisions. This article presents a time‐aware multi‐cyclicqueuing and forwarding scheduling model, that integrates the no‐wait enabled time‐aware shaper and multi‐cyclic queuing and forwarding shaping models. Then, a scheduling solution, dubbed “TSN scheduling optimizer” (TSO) is proposed that combines optimization methods and incremental techniques. TSO aims to balance the load to maximize flow schedulability while guaranteeing the service requirements of hybrid traffic. Simulation evaluations through OMNeT++ provide a performance assessment of this proposed scheduling model, which can satisfy multiple types of traffic transmission requirements. Furthermore, TSO is compared with other baseline scheduling solutions, and TSO shows efficacy regarding execution time and schedulability.

Structure Design Improvement and Stiffness Reinforcement of a Machine Tool through Topology Optimization Based on Machining Characteristics

Machining characteristics were applied to topology optimization for machine tool structure design improvement in this study, and the goals of lightweight and high rigidity of the structure were achieved. Firstly, an ultrasonic-assisted grinding experiment was carried out on zirconia to investigate the surface roughness, surface morphology, grinding vibration, and forces. Then, the topology optimization analysis was conducted for structure design improvement, in which the magnitude of the grinding vibration was utilized as the reference for selecting the topology subsystems and the grinding force was used as the boundary conditions of the static analysis in the topology optimization. Hence, columns, bases, and saddles were redesigned for structure stiffness improvement, and the variations in the effective stress, natural frequency, weight, and stiffness of the whole machine tool were compared accordingly. The results showed that the deduced topological shape (model) can make the natural frequency and stiffness of the whole machine tool tend to be stable and convergent with a weight retention rate more than 75% as the design constraint. The subsystem structures with larger effective stress distributions were designated for stiffness improvement in the design. At the same time, the topological shape (model) was also employed in the design for weight reduction, focusing on minimizing redundant materials within the structure. In contrast to the consistency of the modal shapes before and after topological analysis, the sequential number of the modal mode of the machine tool model after topological analysis was advanced by two modes relative to those of the original situation, which means the original machine tool may be out of its inherently resonant frequency range. Also, the natural frequencies corresponding to each mode had an increasing tendency, and the maximum increase was 110.28%. Furthermore, the stiffness of the machine tool also increased significantly, with a maximum of 355.97%, leading to minor changes of the machine tool’s weight. These results confirm that the topology optimization based on machining characteristics proposed in this study for structure redesign improvement and stiffness enhancement is effective and feasible.

The effect of femur positioning on dual-energy X-ray absorptiometry (DXA) measures and statistical shape and appearance modeling (SSAM) fracture risk assessments.

The diagnosis of osteoporosis using Dual-energy X-ray Absorptiometry (DXA) relies on accurate hip scans, whereby variability in measurements may be introduced by altered patient positioning, as could occur with repeated scans over time. The goal herein was to test how altered postures affect diagnostic metrics (i.e., standard clinical metrics and a newer image processing tool) for femur positioning. A device was built to support cadaveric femurs and adjust their orientation in 3° increments in flexion and internal/external rotation. Seven isolated femurs were scanned in six flexion postures (0° (neutral) to 15° of flexion) and eleven rotational postures (15° external to 15° internal rotation) while collecting standard clinical DXA-based measures for each scan. The fracture risk tool was applied to each scan to calculate fracture risk. Two separate one-way repeated measures ANOVAs (α = 0.05) were performed on the DXA-based measures and fracture risk prediction output. Flexion had a significant effect on T-score, Bone Mineral Density (BMD), and Bone Mineral Content (BMC), but not area, at angles greater than 12°. Internal and external rotation did not have a significant effect on any clinical metric. Fracture risk (as assessed by the image processing tool) was not affected by either rotation mode. Overall, this suggests clinicians can adjust patient posture to accommodate discomfort if deviations are less than 12 degrees, and the greatest care should be taken in flexion. Furthermore, the tool is relatively insensitive to postural adjustments, and as such may be a good option for tracking risk over repeated patient scans.

Wildlife Roadkill Driven by Hydrological Regime in a Subtropical Wetland

This study explored the role of the hydrological regime as a trigger factor for wildlife roadkill along a 22 km road crossing the Taim Wetland, a Ramsar site of international importance in South Brazil. The north–south crossing of BR-471, a federal highway, causes fatalities to numerous animals due to collisions with vehicles. An 11-year long-term time series study encompassing monthly roadkill incidents, water level, and rainfall was analyzed by considering three different periods related to a Wildlife Protection System (WPS): (i) 3 initial years before the WPS implementation (BWPS); (ii) 4 intermediate years after the WPS implementation (AWPS), which includes fences, fauna tunnels, cattle guard stocks, bumps, and speed control; (iii) 4 final years during partial destruction of the WPS (PDWPS). A pseudo-2D full hydrodynamic cell model was employed to fill missing water level data. The model had a good to very good performance (NSE: 0.73 to 0.87; R2: 0.79 to 0.90). The relationship between roadkill incidents and the WPS periods (BWPS, AWPS, and PDWPS) was modeled using Generalized Additive Models for Location, Scale, and Shape (GAMLSS), considering rainfall and water level as predictor variables. The analysis revealed a higher incidence of wildlife roadkill in BWPS compared to AWPS and PDWPS, suggesting the effectiveness of the implemented measures. Critical season and interplay between water levels, rainfall, and the roadkill were assessed. Mammals was the most common roadkill class identified (~92%), followed by reptiles (13%) and birds (2%), with no change in these percentual in the BWPS, AWPS, and PDWPS. Among mammals, capybara (Hydrochoerus hydrochaeris) and coypu (Myocastor coypu) were the most frequent victims (~93% of mammals). Winter, followed by autumn, recorded the highest number of roadkill incidents (>60%), and this pattern remained consistent during the three periods. While rainfall did not emerge as a determining variable for roadkill, water levels above certain thresholds (>3.3 m) drastically diminished the effectiveness of the WPS, mainly due to fauna tunnel submersion. These findings offer valuable insights for enhancing wildlife conservation strategies in this protected area by incorporating hydrological information providing a baseline for designing WPS in similar environments.

Association of Bone Shape and Alignment Analyzed Using Statistical Shape Modeling With Severity of First Metatarsophalangeal Joint Osteoarthritis.

We aimed to explore the relationship between bone shape and radiographic severity in individuals with first metatarsophalangeal joint osteoarthritis (first MTP joint OA). Weightbearing lateral and dorsoplantar radiographs were obtained for the symptomatic foot of 185 participants (105 females, aged 22 to 85 years) with clinically diagnosed first MTP joint OA. Participants were classified into none/mild, moderate, or severe categories using a standardized atlas. An 80-point model for lateral radiographs and 77-point model for dorsoplantar radiographs was used to define independent modes of variation using statistical shape modeling software. Odds ratios adjusted for confounders were calculated using ordinal regression to determine the association between radiographic severity and mode scores. After assessment and grading of radiographs, 35 participants (18.9%) were included in the none/mild first MTP joint OA severity category, 69 (37.2%) in the moderate severity category, and 81 (43.7%) in the severe category. For lateral-view radiographs, 16 modes of variation were included, which collectively represented 83.2% of total shape variance. Of these, four modes were associated with radiographic severity. For dorsoplantar-view radiographs, 15 modes of variation were included, representing 82.6% of total shape variance. Of these, six modes were associated with radiographic severity. Variations in the shape and alignment of the medial cuneiform, first metatarsal, and proximal and distal phalanx of the hallux are significantly associated with radiographic severity of first MTP joint OA. Prospective studies are required to determine whether bone shape characteristics are associated with the development and/or progression of this condition.

An attempt to correct the underestimation of inequality measures in cross-survey imputation through generalized additive models for location, scale and shape

This paper contributes to the debate on ways to improve the calculation of inequality measures in developing countries experiencing severe budget constraints. Linear regression-based survey-to-survey imputation techniques (SSITs) are most frequently discussed in the literature. These are effective at estimating predictions of poverty indicators but are much less accurate with inequality indicators. To demonstrate this limited accuracy, the first part of the paper review and discuss the SSITs. The paper proposes a method for overcoming these limitations based on a Generalized Additive Models for Location, Scale and Shape (GAMLSS). Before to apply this method to Moroccan data with the aim to analyze the relation between poverty and climate changes a simulation is carried out to compare classical SSIT and SSIT based on GAMLSS.

Performance of compound parabolic concentrator solar air flat plate collector using phase change material

This work involves an experimental investigation on two different new models of Compound Parabolic Concentrator (CPC) solar air flat plate collector with adding paraffin wax Phase Change Material (PCM). The first model is called involute shape model, whereas the second model is called normal shape model. To explore the best model performance, the structures of compound parabolic concentrator models have two symmetric giant parabolic mirror reflectors, similar concentration ratio of 1.7, similar flat plate receiver with 12 tubes filled by paraffin wax phase change material. The performances of both models are presented together for purpose of comparison. The tests have been performed in April and May 2023 in Mosul city/Iraq under standard conditions for around 11 h during the day. The outcomes indicated that rising in air mass flowrate leads to rise the receiver performance. The findings confirm that the thermal efficiency of normal shape is more efficient than involute shape by 7.6%, 13%, and 11.7% for 0.0085 kg/s, 0.0174 kg/s, and 0.027 kg/s, respectively. The optimum thermal performances for involute and normal shapes have been obtained equal 65.4% and 77.1% for 0.027 kg/s, respectively. For constant air flowrate, the performance of normal shape is higher than involute shape by 15.5% (14th May), 12.6% (27th April) and 11.8% (15th April). A significant temperature change has been found in involute shape as compared to normal shape. The outlet temperature of normal shape is always lower than the involute shape for all air mass flowrate. It is demonstrated that phase change material and the position of the receiver have considerable influence on the model performance. The current work provides an important information for evaluating the compound parabolic concentrator model performance for Mosul/city.

Impact of data synthesis strategies for the classification of craniosynostosis.

Photogrammetric surface scans provide a radiation-free option to assess and classify craniosynostosis. Due to the low prevalence of craniosynostosis and high patient restrictions, clinical data are rare. Synthetic data could support or even replace clinical data for the classification of craniosynostosis, but this has never been studied systematically. We tested the combinations of three different synthetic data sources: a statistical shape model (SSM), a generative adversarial network (GAN), and image-based principal component analysis for a convolutional neural network (CNN)-based classification of craniosynostosis. The CNN is trained only on synthetic data but is validated and tested on clinical data. The combination of an SSM and a GAN achieved an accuracy of 0.960 and an F1 score of 0.928 on the unseen test set. The difference to training on clinical data was smaller than 0.01. Including a second image modality improved classification performance for all data sources. Without a single clinical training sample, a CNN was able to classify head deformities with similar accuracy as if it was trained on clinical data. Using multiple data sources was key for a good classification based on synthetic data alone. Synthetic data might play an important future role in the assessment of craniosynostosis.

Evolution of cross-sectional shapes of liquid inclusions enclosed in a crystal: Computer simulation and its application for studying interface kinetics

The previously developed model of the cross-sectional stationary shape of a liquid inclusion migrating through a non-uniformly heated crystal has been extended to the case of transient processes caused by changes in the temperature gradient value and/or temperature oscillations. On the basis of computer simulations, the evolution of the cross-sectional shapes of the inclusions with 4 facets in their cross sections has been analyzed for the cases of different mechanisms of interfacial processes. A technique for estimating kinetic coefficients from the geometry of the inclusion shapes established after imposing and removing the temperature gradient has been proposed. The possibility of controlling the velocity and shape of the 4-faceted inclusions by means of temperature oscillations has been demonstrated and discussed.

Analysis of Short Video Communication Mode of City Image under Cross-cultural Threshold: Taking Chengdu on Tiktok as an Example

Cities are carriers of culture, and the process of shaping and disseminating city image is also the process of exporting Chinese culture to foreign countries and attracting audiences. TikTok, as an overseas short-video sharing platform with the characteristics of fragmentation, interactivity, and fast dissemination, has attracted widespread attention from overseas audiences for its short videos about Chinese city image and culture, which has become an important channel for cross-cultural communication. This paper takes the international image shaping and dissemination model of the popular Chinese city Chengdu on TikTok as the research object and uses the 5W model and the encoding decoding theory as the theoretical basis to explore the strategies and effects of Chinese city image and culture dissemination on TikTok from a cross-cultural perspective. The study shows that Chengdus communication on TikTok is characterized by self-media as the major communication body, young travelers as the target audience, the use of city branding and other shaping perspectives as the content communication strategy, and the use of emotional resonance and cultural interactions to enhance the appeal and communication effect of the city, which provides some references and guidance to Chinas external communication of its city image.

Rectifying Strip Patterns

Straight flat strips of inextensible material can be bent into curved strips aligned with arbitrary space curves. The large shape variety of these so-called rectifying strips makes them candidates for shape modeling, especially in applications such as architecture where simple elements are preferred for the fabrication of complex shapes. In this paper, we provide computational tools for the design of shapes from rectifying strips. They can form various patterns and fulfill constraints which are required for specific applications such as gridshells or shading systems. The methodology is based on discrete models of rectifying strips, a discrete level-set formulation and optimization-based constrained mesh design and editing. We also analyse the geometry at nodes and present remarkable quadrilateral arrangements of rectifying strips with torsion-free nodes.

RAPID AND AUTOMATED BODY MEASUREMENT OF CATTLE BASED ON STATISTICAL SHAPE MODEL

Abstract. The current methods of non-contact livestock body measurement directly deal with the low-quality point cloud data of livestock, which have low robustness and lack practicality. On the one hand, the success rate of keypoint detection for livestock body measurement is low. Due to the severe occlusion and noise in the point cloud data, body measurements of some data cannot be performed. On the other hand, the key frames need to be manually selected from the point cloud sequence during processing. Inspired by the work of 3D reconstruction based on animal statistical shape models, we implement the construction and learning of the statistical shape model of real cattle. Given the establishment of the statistical shape model of cattle, a 3D reconstruction and body measurement approach of real cattle based on low-quality point cloud data is proposed. Nine indicators are calculated and the overall estimation MAPE (Mean Absolute Percentage Error) is 10.27%. The whole process of the body measurement algorithm proposed in our paper can be extended to other quadrupeds.

Super-quadric CFD-DEM-VOF modelling of gas–solid-liquid systems

In many chemical engineering processes, gas–solid-liquid flow involving nonspherical solid particles is commonly practised and should be modelled for process understanding and optimisation; however, reliable modelling along with parallelization is still lacking. This work combines an unresolved CFD-DEM-VOF coupling framework, featuring a super-quadric particle shape model to model the gas-nonspherical particle-liquid flow and a “ghost domain” algorithm to improve the parallelization efficiency. The model is applied to two cases – cuboid particle sedimentation and dambreak formation, for model effectiveness demonstration. According to the simulation results, the model effectively captures the interactions between particles and phases in the relevant scenarios. This is reflected by the agreements of numerical simulations and experimental or analytical data in these two cases. Moreover, the parallelization technique utilizing the “ghost domain” displays remarkable steadiness and noteworthy efficiency. As the processor number increases from 2, 4, 8, 16, to 32, the running time obviously decreases almost linearly. This research introduces an innovative computational approach for simulating systems containing gas, non-spherical particles, and liquid. It demonstrates the method’s potential to analyze interactions between particles and fluids in the unresolved framework.

Cohort-Based Reference Values for Serum Ferritin and Transferrin and Longitudinal Determinants of Iron Status in European Children Aged 3–15 Years

BackgroundReference values of ferritin and transferrin for European children do not exist. ObjectiveWe aimed to provide sex-, age-, and body mass index (BMI)-specific serum ferritin and transferrin reference percentiles of 3–15-y-old children based on cohort data and to investigate determinants of iron status. MethodsA total of 3390 ferritin and 3416 transferrin measurements from children residing in 8 European countries participating in the IDEFICS/I.Family cohort (https://www.isrctn.com/ISRCTN62310987) at baseline (W0) and 6 y later (W3) were used to estimate percentiles using the generalized additive model for location, scale and shape. Associations of serum ferritin and transferrin concentrations with total iron intake, total iron intake additionally adjusted for vitamin C intake, and iron from heme sources were investigated separately with adjustment for sex, age, country of residence, parental education, usual energy intake and BMI z-score in regression models using cross-sectional and longitudinal data. ResultsThe age-specific ferritin and transferrin 5th and 95th reference percentiles ranged from 10.9 to 81.1 μg/L and 2.23 to 3.56 g/L, respectively. A deficient iron status was observed in 3% of children at W0 and 7% of children and adolescents at W3, respectively. At both waves, a higher iron intake from heme sources was positively associated with serum ferritin {W0: β = 3.21 [95% confidence interval (CI): 0.71, 5.71]; W3: β = 4.48 [95% CI: 2.09, 6.87]}, that is, children consuming one mg more heme iron had a 3.21 and 4.48 μg/L higher ferritin concentration. Adherence to a mainly vegetarian diet was associated with a lower chance for sufficient serum ferritin cross-sectionally at W3 [odds ratio (OR) 0.40 (95% CI: 0.21, 0.81)] and longitudinally [OR 0.35 (95% CI: 0.15, 0.93)]. ConclusionsAge-, sex-, and BMI-specific reference percentiles of serum ferritin and transferrin concentrations based on cohort data are provided for European children aged 3–15 y and may be used in clinical practice.

FaceScape: 3D Facial Dataset and Benchmark for Single-View 3D Face Reconstruction.

In this paper, we present a large-scale detailed 3D face dataset, FaceScape, and the corresponding benchmark to evaluate single-view facial 3D reconstruction. By training on FaceScape data, a novel algorithm is proposed to predict elaborate riggable 3D face models from a single image input. FaceScape dataset releases 16,940 textured 3D faces, captured from 847 subjects and each with 20 specific expressions. The 3D models contain the pore-level facial geometry that is also processed to be topologically uniform. These fine 3D facial models can be represented as a 3D morphable model for coarse shapes and displacement maps for detailed geometry. Taking advantage of the large-scale and high-accuracy dataset, a novel algorithm is further proposed to learn the expression-specific dynamic details using a deep neural network. The learned relationship serves as the foundation of our 3D face prediction system from a single image input. Different from most previous methods, our predicted 3D models are riggable with highly detailed geometry under different expressions. We also use FaceScape data to generate the in-the-wild and in-the-lab benchmark to evaluate recent methods of single-view face reconstruction. The accuracy is reported and analyzed on the dimensions of camera pose and focal length, which provides a faithful and comprehensive evaluation and reveals new challenges. The unprecedented dataset, benchmark, and code have been released to the public for research purpose.

Facilitating Clinical Use of the Amsterdam Instrumental Activities of Daily Living Questionnaire: Normative Data and Diagnostic Cutoff Values

AbstractBackgroundThe Amsterdam Instrumental Activities of Daily Living Questionnaire(A‐IADL‐Q) measures impairments in everyday functioning in the context of dementia. To facilitate interpretation and clinical implementation of the A‐IADL‐Q, we aimed to generate demographically‐adjusted norm scores and a clinical cutoff value.MethodCross‐sectional data were used from Dutch Brain Research Registry(N = 1,064; mean (M) age = 62±11year; 69.5% female) European Medial Information Framework‐Alzheimer’s Disease(EMIF‐AD) 90+ (N = 63; Mage = 92±2year; 52.4% female), and European Prevention of Alzheimer’s Dementia Longitudinal Cohort Study(EPAD‐LCS)(N = 300; Mage = 62±7year; 71.7% female)(Table 1). Norm scores were generated using a I) linear regression model approach; and II) Box‐Cox Power Exponential(BCPE) model approach (the latter falling within the generalized model for location shape and size(GAMLSS) framework). For both methods, different combinations of the variables age, sex and education were tested (including polynomial age and age*education interaction) and the best model was selected based on the lowest Aikake Information Criteria(AIC) value. Next, diagnostic contrast was generated using an independent memory clinic sample (the Amsterdam Dementia Cohort(ADC)(N = 2,123, Mage = 64±8year, 46.1% female)(Table 1), including people with dementia (both Alzheimer’s Disease(AD) and non‐AD). The optimal cut‐off value for cognitively healthy versus dementia was determined, and corresponding diagnostic accuracy was calculated using area under the receiver operating curves(AUC‐ROC). This optimal cutoff was then applied to different diagnostic contrasts, including mild cognitive impairment(MCI) and subjective cognitive decline(SCD).ResultThe best suitable normative model was the linear regression model, including cubic polynomial for age, education, and age*education interaction. This model was considered superior to the best BCPE model, which had infinitely large residuals due to bad model fit. The optimal cutoff to distinguish normal functioning from dementia, based on bootstrap average estimates of the Youden Index, was 1.82 standard deviation(SD) below the population mean, resulting in an AUC of 0.97[95%CI 0.98‐0.98]). Normative data are made publicly available in an online tool(Fig.1).ConclusionWe established demographically adjusted norm scores for the A‐IADL‐Q using a large representative sample, and provided an optimal cutoff value for cognitively healthy versus dementia, with outstanding accuracy. Our results, captured in the online tool, can help clinicians to interpret A‐IADL‐Q scores more easily by allowing comparison of functioning against a reference population.

Effect of Mesh Shape and Turbulence Model on Aerodynamic Performance at NACA 4415

This study uses three turbulence model variations, i.e., S - A, k - ε, and k – ω turbulence models. In addition, there are two variations of cell shape and three variations of cell number. The number of cells is 500, 5000, 50000, and 100000. Verification is carried out in the mesh refinement study and validated by aerodynamic performances. Based on the mesh refinement study, quadrilateral cells with the k - ε are in the asymptotic convergence range. Based on the Cl, it can be concluded that the quadrilateral mesh with 50000 and 100000 cells simulated using the k-ε turbulence model shows very low errors, namely 4.1151% and 3.8643%, respectively. It shows consistency based on the quadrilaterals Cd mesh data with the k-ε and k-ω turbulence models. However, k-ε shows the lowest error with the number of cells 50000 and 100000, i.e., 127.7682% and 110.4175%, respectively. However, choosing mesh 50000 cells are advisable because it only takes 23 minutes 48 seconds in computation, while mesh 100000 cells take 1 hour 17 minutes 21 seconds. Only Cm from quadrilateral mesh with the turbulence model k-ω shows consistency. An error of mesh 50000 cells is 22.0717%, and the error value for 100000 cells is 18.1630%. By considering computation time, mesh 50000 cells are preferable because it only takes 27 minutes 16 seconds, which is faster 43 minutes 14 seconds than 100000 cells.

Observed and modeled moulin heads in the Pâkitsoq region of Greenland suggest subglacial channel network effects

Abstract. In the ablation zone of land-terminating areas of the Greenland Ice Sheet, water pressures at the bed control seasonal and daily ice motion variability. During the melt season, large amounts of surface meltwater access the bed through moulins, which sustain an efficient channelized subglacial system. Water pressure within these subglacial channels can be inferred by measuring the hydraulic head within moulins. However, moulin head data are rare, and subglacial hydrology models that simulate water pressure fluctuations require water storage in moulins or subglacial channels. Neither the volume nor the location of such water storage is currently well constrained. Here, we use the Moulin Shape (MouSh) model, which quantifies time-evolving englacial storage, coupled with a subglacial channel model to simulate head measurements from a small moulin in Pâkitosq, western Greenland. We force the model with surface meltwater input calculated using field-acquired weather data. Our first-order simulations of moulin hydraulic head either overpredict the diurnal range of oscillation of the moulin head or require an unrealistically large moulin size to reproduce observed head oscillation ranges. We find that to accurately match field observations of moulin head, additional subglacial water must be added to the system. This subglacial baseflow is likely sourced from basal melt and nonlocal surface water inputs upstream. We hypothesize that the additional baseflow represents strong subglacial network connectivity throughout the channelized system and is consistent with our small moulin likely connecting to a higher-order subglacial channel.