3D Geometry Research Articles

DeepJEB: 3D Deep Learning-based Synthetic Jet Engine Bracket Dataset

Abstract Recent advances in artificial intelligence (AI) have impacted various fields, including mechanical engineering. However, the development of diverse, high-quality datasets for structural analysis remains a challenge. Traditional datasets, like the jet engine bracket dataset, are limited by small sample sizes, hindering the creation of robust surrogate models. This study introduces the Deep- JEB dataset, generated through deep generative models and automated simulation pipelines, to address these limitations. DeepJEB offers comprehensive 3D geometries and corresponding structural analysis data. Key experiments validated its effectiveness, showing significant improvements in surrogate model performance. Models trained on DeepJEB achieved up to a 23% increase in the coefficient of determination and over a 70% reduction in mean absolute percentage error (MAPE) compared to those trained on traditional datasets. These results underscore the superior generalization capabilities of DeepJEB. By supporting advanced modeling techniques, such as graph neural networks (GNNs) and convolutional neural networks (CNNs), DeepJEB enables more accurate predictions in structural performance. The DeepJEB dataset is publicly accessible at: https://www.narnia.ai/dataset.

Three-Dimensional Hullform Reconstruction from Two-Dimensional Drawings Based on Image Processing Techniques

Abstract Repair shipyards sometimes need three-dimensional geometry for repairs and retrofits. However, they often create models manually from two-dimensional drawings provided by ship owners. In this case, human error leads to inaccuracies, making the process time-consuming and laborious. Therefore, there is a need for research on efficient three-dimensional hullform reconstruction from two-dimensional drawings. This study proposed a method to automatically extract points from two-dimensional lines and visualize them in three dimensions. The proposed method consists of three steps. The first step is a point extraction through image processing, which uses a starting point search algorithm to access overlapping or intersection lines and extracts the points on the lines in the drawing by searching for paths between the starting point and the end point entered by the user. The second step is the transformation of the extracted data, which transforms the points based on pixel coordinate into 3D points through coordinate transformation and scaling by utilizing the stored line data and three-dimensional coordinate information. The last step is to visualize the transformed data as a real three-dimensional model with point visualization. This study demonstrates that the proposed method can be effectively utilized by detecting two-dimensional lines and reconstructing the hullform in three dimensions.

Evaluating process parameters of SLS part of polyamide-12 recycled powder using RSM

Abstract Selective laser sintering (SLS) is a highly flexible rapid prototyping (RP) technology which can use a wide range of powdered materials to generate high quality complex geometries directly from digital models. The SLS process uses a laser to sinter selected areas of powdered materials to produce solid objects layer by layer, according to the 2D data obtained from CT scan or MRI imaging techniques. This paper presents research work for evaluating the effect of process parameters on impact toughness and surface roughness of the part manufactured on Farsoon SS402P SLS from recycled ageing FS 3300PA which is PA-12 based powder and standard operating parameters using Response Surface Methodology (RSM) design of experiments. Impact tests have been performed as per the ASTM D6110 standard using recommendations from ISO 179-2. According to the results, effect of process parameters are successfully analyzed by employing impact toughness and surface roughness tests. Based on the results of the ANOVA analysis, impact toughness increases with increasing laser power (4.467 j/cm2 at 72.5 watts). Similarly, it is also clear from the results that the region with the lowest laser power, high layer thickness, and 90-degree orientation had the lowest surface roughness (3.46 μm).

Geometric decomposition and algorithmic model of main gate muqarnas of Ankara Cenabi Ahmet Pasha Mosque

Cenabi Ahmet Pasha Mosque (Cenab-ı Ahmet Paşa Mosque) is known to be the only work that resembles the style of Architect Sinan in Ankara. In this study, it is aimed to examine the two-dimensional muqarnas scheme of the mosque's main gate and to determine through algorithmic modeling the combination of muqarnas cells in the third dimension. By the photogrammetry method, the grid and star forms used in the muqarnas plan scheme are revealed. The geometric characteristic of the muqarnas in two dimensions is determined. Cell types of the muqarnas and their geometric configurations are obtained. It is demonstrated that there is a radial grid in the plan of the Cenabi Ahmet Pasha Mosque main gate muqarnas, and that the three-dimensional structure of the muqarnas consisting of six layers includes almond cell, triangle cell, deltoid cell, intermediate triangle cell, intermediate deltoid cell, biped cell and triped cell. Additionally, an algorithmic model based on the creation of cell unit system is proposed for the creation of the muqarnas geometry in 3D.

Diffusion-based generative drug-like molecular editing with chemical natural language

Recently, diffusion models have emerged as a promising paradigm for molecular design and optimization. However, most diffusion-based molecular generative models focus on modeling 2D graphs or 3D geometries, with limited research on molecular sequence diffusion models. The International Union of Pure and Applied Chemistry (IUPAC) names are more akin to chemical natural language than the Simplified Molecular Input Line Entry System (SMILES) for organic compounds. In this work, we apply an IUPAC-guided conditional diffusion model to facilitate molecular editing from chemical natural language to chemical language (SMILES) and explore whether the pre-trained generative performance of diffusion models can be transferred to chemical natural language. We propose DiffIUPAC, a controllable molecular editing diffusion model that converts IUPAC names to SMILES strings. Evaluation results demonstrate that our model outperforms existing methods and successfully captures the semantic rules of both chemical languages. Chemical space and scaffold analysis show that the model can generate similar compounds with diverse scaffolds within the specified constraints. Additionally, to illustrate the model's applicability in drug design, we conducted case studies in functional group editing, analogue design and linker design.

Computational multiphysics modeling of radioactive aerosol deposition in diverse human respiratory tract geometries

The evaluation of aerosol exposure relies on generic mathematical models that assume uniform particle deposition profiles over the human respiratory tract and do not account for subject-specific characteristics. Here we introduce a hybrid-automated computational workflow that generates personalized particle deposition profiles in 3D reconstructed human airways from computed tomography scans using Computational Fluid and Particle Dynamics simulations. This is the first large-scale study to consider realistic airways variability, where 380 lower and 40 upper human respiratory tract 3D geometries are reconstructed and parameterized. The data is clustered into nine groups using random forest regression. Computational fluid and particle dynamics simulations are conducted on these representative geometries using a realistic heavy-breathing respiratory cycle and radioactive iodine-131 as a source term. Monte Carlo radiation transport simulations are performed to obtain detailed energy deposition maps. Our findings emphasize the importance of personalized studies, as minor respiratory tract variations notably influence deposition patterns rather than global parameters of the lower airways, observing more than 30% variance in the mass deposition fraction.

Thoracic Aortic Three-Dimensional Geometry.

Aortic structure impacts cardiovascular health through multiple mechanisms. Aortic structural degeneration occurs with aging, increasing left ventricular afterload and promoting increased arterial pulsatility and target organ damage. Despite the impact of aortic structure on cardiovascular health, three-dimensional (3D) aortic geometry has not been comprehensively characterized in large populations. We segmented the complete thoracic aorta using a deep learning architecture and used morphological image operations to extract multiple aortic geometric phenotypes (AGPs, including diameter, length, curvature, and tortuosity) across various subsegments of the thoracic aorta. We deployed our segmentation approach on imaging scans from 54,241 participants in the UK Biobank and 8,456 participants in the Penn Medicine Biobank. Our method provides a fully automated approach towards quantifying the three-dimensional structural parameters of the aorta. This approach expands the available phenotypes in two large representative biobanks and will allow large-scale studies to elucidate the biology and clinical consequences of aortic degeneration related to aging and disease states.

Joint Observation of a Series of Magnetic Holes by Tianwen-1 and MAVEN on Mars

Magnetic holes (MHs) are transient magnetic structures responsible for energy conversion in space plasma. Using single-spacecraft measurements from Mars Atmosphere and Volatile EvolutioN (MAVEN), the existence of MHs on Mars has been confirmed. However, due to the limitations of single-spacecraft observations, significant uncertainty also arises on the identification of the spatial scale and 3D geometry of MHs. In this study, we report a series of MHs successively detected by Tianwen-1 near the high-latitude magnetopause and by the MAVEN spacecraft near the subsolar magnetopause. The large separation between Tianwen-1 and MAVEN (∼4 R M) suggests these MHs are macroscale structures extending along the axial direction. Additionally, we observe whistler waves generated by electron perpendicular anisotropy in one of the macroscale MHs. This study is the first joint observation of Martian MHs, shedding light on the research of transient magnetic structures on Mars.

Machine Vision to Provide Quantitative Analysis of Meltpool Stability for a Coaxial Wire Directed Energy Deposition Process

Wire-based additive manufacturing (AM) is at the forefront of complex metal fabrication because of its scalability for large components, potential for high deposition rates, and ease of use. A common goal of wire directed energy deposition (DED) is preserving a stable process throughout deposition. If too little energy is put into the deposition, the wire will stub into the substrate and begin oscillating, creating turbulence within the meltpool. If too much energy exists, the wire will overheat, causing surface tension to take over and create liquid drips as opposed to a solid bead. This paper proposes a computer vision technique to work as both a state detection and event detection system for wire stability. The model utilizes intensity variations along with frame-to-frame difference calculations to determine process stability. Because the proposed model does not rely on machine learning techniques, it is possible for an individual to interpret and adjust as they see fit. The first part of this paper describes creation and implementation of the model. The model’s capability was then evaluated using a 1D laser power experiment, which generated a wide range of stability states across varying powers. The model’s accuracy was evaluated through 3D geometry data gathered from the experimentally deposited beads. The model proved to be both capable and accurate and has potential to be used as a real-time control system with future work.

Oriblock: The origami-blocks based on hinged dissection

Traditional origami, a well-known art of paper-folding, does not account for material thickness. Subsequently, several thickness-accommodation techniques have been developed, considering the non-negligible thickness of materials, which enables the application of origami to a wide range of mechanisms. Although these techniques prevent self-intersections and preserve kinematics after accommodating panel thickness, they are limited by their reliance on initial zero-thickness origami patterns in two dimensions. To address this issue, this study proposes a novel technique, the volume-accommodation technique in three-dimensional space, which allocates non-coplanar origami creases based on solid geometry to allow a greater variety of shape-changing motions. Typical solid geometries, such as a pyramid and a prism, are selected as examples to demonstrate the design procedures. Closure equations of the obtained oriblock are used to analyze the kinematic properties. Additionally, Bennett and Myard Linkages are derived from oriblocks based on their kinematic equivalence to strengthen their connections. As a result, this new type of origami can offer a variety of mechanisms for designers and facilitate potential applications in origami-inspired metamaterials and robotics.

Multi-functional additively constructed honeycomb walls for housing and sheltering

Honeycomb designs have become increasingly popular in additive manufacturing due to their great mechanical properties. The honeycomb infill pattern has allowed the creation of lightweight objects without sacrificing the strength of solid objects. Honeycombs have been utilized in different applications, including architecture, aerospace, and automotive. However, there is a lack of honeycomb designs within the construction industry. With the growth of concrete additive construction, there is an opportunity to introduce honeycomb designs into construction practices. This paper proposes a methodology for manufacturing multi-functional walls through additive construction. The methodology includes several key steps, from material development and quality control testing to wall assembly and experimental investigation. Two multi-functional wall segments were additively constructed, reaching a height of 1180 mm, a width of 610 mm, and a thickness of 90 mm. These measurements led to the manufacturing of seven distinct cellular sections per wall, comprising three main, two intermediate, and two end cellular units. In addition, a columnar unit was additively constructed for corner connection, featuring two vertical octagonal cells with end brackets. A preliminary wall system made up of honeycomb wall segments was assembled using various additional materials such as sealant, clamping systems, and insulative materials. Furthermore, an additional wall segment with end connections was designed and manufactured to test its load-carrying capacity. The wall segment performed well compared to wooden stud walls and showed sufficient load-carrying capacity. Unlike traditional un-reinforced concrete structures, which are brittle and experience small plastic deformations before failing, the honeycomb wall structure experienced large plastic deformation before failure due to the use of insulation foam and silicon rubber. The preliminary testing showed that the honeycomb wall system could be an alternative to traditional housing construction, especially for shelters.

Research on wheel–rail impact dynamics due to combined rail weld irregularities and polygonal wheel effects considering 3D contact geometry

The geometric irregularities of rail welds and polygonal wheels are common defects observed in high-speed railways, leading to the generation of high-frequency impact forces and vibrations in the wheel–rail contact system. This research establishes a novel vehicle-track coupled dynamic model that incorporates a 3D wheel–rail contact model using meshing grid and conjugate gradient methods to study the high-magnitude wheel–rail impact forces caused by rail weld irregularities and polygonal wheel combinations. The primary objective of this study is to develop a precise 3D contact model that incorporates the actual geometry of wheel and rail surface irregularities into the calculation of vehicle-track dynamic interactions. The study evaluates the effects of 3D rail weld irregularities and polygonal wheels on wheel–rail dynamic interaction by comparing results with those obtained from a conventional vehicle-track coupled model that considers a 2D contact model. The results show that the lengths and depths of polygonal wheels and rail weld irregularities, as well as vehicle speeds, significantly increase the wheel/rail dynamic impact forces. The results also indicate that the widening of irregularities not only significantly affects the wheel–rail contact force but also has an important influence on the contact stiffness.

Biodegradable Citrate‐Based Polymers Enable 5D Monitoring of Implant Evolution

AbstractBiodegradable tissue engineering scaffolds have garnered increasing interest for their role in providing mechanical support, promoting tissue regeneration, and eliminating the need for removal. However, the in vivo degradation processes remain challenging to track. Here, a novel biodegradable polymer, N‐methyldiethanolamine (MDEA) and Gadolinium(III) diethylenetriamine pentaacetate (Gd‐DTPA) modified biodegradable photoluminescent polymers (BPLPMGd), which combines near‐infrared (NIR) fluorescence and magnetic resonance (MR) dual‐modality imaging are introduced to monitor scaffold degradation in vivo. The chemical structure of BPLPMGd is characterized and its dual‐imaging properties in vitro are evaluated. Subsequently, non‐invasive dual‐modality imaging to track the degradation of implanted BPLPMGd scaffolds is performed in a rat model, comparing these results with histological data. This approach reveals that BPLPMGd enables reliable non‐invasive tracking of the degradation, where NIR fluorescence imaging offers a qualitative and quantitative analysis of scaffold mass loss, total volume and solid content changes, while magnetic resonance imaging (MRI) details structural and morphological changes, allowing for 5D monitoring of implant degradation, including 3D structure, location, mass, volume, and geometry. The combination of these imaging modalities provides a comprehensive view of scaffold degradation, where the synergistic use of both yields results greater than either modality alone, offering unprecedented 5D information of implantable devices. This innovative approach has potential applications in regenerative engineering and beyond.

3D modelling-based left atrium geometry analysis to predict atrial fibrillation in patients with cryptogenic stroke

Abstract Introduction Atrial fibrillation (AF) is a high prevalence condition associated with high morbidity and mortality. Its early diagnosis allows to initiate several prophylactic treatments that have proved to improve its prognosis. 3D modeling techniques allow to design accurate representations of cardiac cavities from diverse source images. Purpose We evaluated the association between new geometrical measurements obtained by 3D echocardiographic models of the left atrium (LA) and the presence of underlying paroxysmal AF in patients with cryptogenic stroke (CS). Methods 3D LA volumes from a sample of consecutive patients aged ≥60 years old and admitted to our Stroke Unit with the diagnosis of CS from April 2019 to November 2020 were analyzed. 15 days ECG monitoring was performed after discharge to detect underlying paroxysmal AF. All 3D echo volumes were analyzed by a 3D modeling open-source application. From this analysis several LA geometry parameters were obtained: sphericity, elongation, flatness, major axis length, minor axis length, maximum 2D diameter (max2Ddiam) and surface-volume ratio (S/V). Functional atrial parameters (3D left atrial ejection fraction, LAEF) were also determined. Results 36 patients were analyzed. Mean age was 78 ± 7 years old. 19% were diabetic, 58% had high blood pressure and 14% of patients had history of ischemic cardiomyopathy. Mean body-mass index was 26. These baseline characteristics were equally distributed between groups. Baseline proBNP was significantly higher in patients developing atrial fibrillation (949 vs 275 pg/mL; p= 0,014.). Maximum 2D diameter (max2Ddiam) and Surface-volume ratio (S/V) were the best predictors of AF (AUC of ROC curves: 0.69; 0.49 – 0.88 and 0.63; 0.42 – 0.84, respectively). An optimal cut-off point of 39 mm was selected for max2Ddiam and of 0.20 for S/V. These values were included in a multivariate model adjusted by baseline proBNP. A Max2Ddiam <39 mm and a S/V ≥0.20 were both significantly associated with the presence of paroxysmal AF (OR 6.96; p= 0.048 and OR 12.22; p = 0.043, respectively). Conclusions New geometrical atrial parameters can be obtained from echo 3D models and might be able to predict AF in patients with CS. Further analysis and larger samples are required to better understand these findings.TableFigure

Relationship between 3-dimensional aortic geometry and the risk of new-onset heart failure

Abstract Introduction Aortic geometric assessments to date have typically involved cross-sectional imaging of discrete aortic segments. However, the complex 3D geometry of the thoracic aorta, and its relationship to the risk of cardiovascular disease has not been assessed in large studies. Methods We segmented the thoracic aorta in SSFP axial MRI images from 49,282 UK Biobank participants using a deep convolutional neural network (U-Net). We then computed geometric measurements (centerline length, arch height, arch width, arch height-width ratio, arch unfolding index [inverse of tortuosity], curvature and mean, minimum and maximum diameters) in the overall thoracic aorta as well as in 6 subsegments. We quantified their relationship to the risk of cardiomyopathy and heart failure (HF) using multivariable Cox-proportional hazards models. All hazard ratios are standardized. Centerline length was allometrically indexed to body height. Results In models adjusted for age, sex, BMI kg/m2, low-density lipoprotein mg/dl, high-density lipoprotein mg/dl, triglycerides mg/dl, systolic blood pressure and diastolic blood pressure, the curvature of the aorta (HR=0.63; 95% CI=0.39, 0.99; P<0.05), particularly the aortic arch (HR=0.54; 95% CI= 0.35, 0.87; P<0.05) was strongly related to a lower risk of incident cardiomyopathy. A higher arch unfolding index (HR=1.46; 95% CI=1.03, 2.07; P<0.05), higher width-to-height ratio (HR=1.10; 95% CI= 1.02, 1.19; P<0.05) and lower aorta tortuosity (HR=1.20; 95% CI= 1.09, 1.32; P<0.05) were all associated with a higher risk of cardiomyopathy. This aortic geometric pattern was also associated with a greater risk of a new HF diagnosis (arch unfolding [HR=1.23; 95% CI= 1.07, 1.42; P<0.05], width to heigh ratio [HR=1.06; 95% CI= 1.01, 1.11; P0.05]). Conclusion We demonstrate that 3-D thoracic aortic geometric indices are strongly associated with incident cardiomyopathy and HF, independent of classic risk factors. This is likely related to abnormal afterload and ventricular-arterial interactions since adverse geometric remodeling affects left ventricular pulsatile load.Cardiomyopathy & Heart Failure Hazards

3D geometry of the Dugald River Shear Zone, Mount Isa Inlier, Australia

The Dugald River Shear Zone is a structurally complex, anastomosing shear zone that hosts high-grade Zn mineralisation within the Dugald River Slate. The Dugald River Zn–Pb–Ag mine is situated within the Mount Isa Inlier, Australia and developed through two phases of mineralisation with high-grade Zn mineralisation intimately associated with the development of the brittle–ductile Dugald River Shear Zone. The first phase of mineralisation occurred during the regionally extensive, D2 ductile fold and axial planar cleavage forming event and resulted in the development of a sulfide horizon. This horizon was a preferential site for strain partitioning during early D4 ductile deformation, which resulted in transposition-related concentration and thickening of a sulfide horizon marking the second phase of mineralisation. The Dugald River Shear Zone developed during D4 wherein strain rate incompatibilities between a ductile-deforming sulfide horizon and the brittle-deforming Dugald River Slate resulted in the development of a Riedel shear zone. A high-resolution 3D model of the shear zone was constructed from robust drilling and mapping datasets in which releasing and restraining bends highlight the thickening and thinning of ore lenses, respectively. The 3D model with detailed structural analysis and observations allows for predictive modelling of dilational zones within throughgoing Y-shears, which are prospective sites for remobilisation of high-grade Zn sulfides as the ore body developed coevally with progressive shearing.

Joint Optimization-Based Texture and Geometry Enhancement Method for Single-Image-Based 3D Content Creation

Recent studies have explored the generation of three-dimensional (3D) meshes from single images. A key challenge in this area is the difficulty of improving both the generalization and detail simultaneously in 3D mesh generation. To address this issue, existing methods utilize fixed-resolution mesh features to train networks for generalization. This approach is capable of generating the overall 3D shape without limitations on object categories. However, the generated shape often exhibits a blurred surface and suffers from suboptimal texture resolution due to the fixed-resolution mesh features. In this study, we propose a joint optimization method that enhances geometry and texture by integrating generalized 3D mesh generation with adjustable mesh resolution. Specifically, we apply an inverse-rendering-based remeshing technique that enables the estimation of complex-shaped mesh estimations without relying on fixed-resolution structures. After remeshing, we enhance the texture to improve the detailed quality of the remeshed mesh via a texture enhancement diffusion model. By separating the tasks of generalization, detailed geometry estimation, and texture enhancement and adapting different target features for each specific network, the proposed joint optimization method effectively addresses the characteristics of individual objects, resulting in increased surface detail and the generation of high-quality textures. Experimental results on the Google Scanned Objects and ShapeNet datasets demonstrate that the proposed method significantly improves the accuracy of 3D geometry and texture estimation, as evaluated by the PSNR, SSIM, LPIPS, and CD metrics.

Numerical Investigation of Two-Phase Shock Waves in CO2 Flows Using a Modified Hertz–Knudsen Model

Abstract Understanding the complex behavior of two-phase shocks in CO2 flows is essential for a variety of applications, including carbon capture and storage () and transcritical refrigeration cycles. This study presents a comprehensive numerical investigation of two-phase shock waves using the multispecies user-defined real gas model in Ansys Fluent. The simulations are performed for de-Laval nozzles, exploring the two-phase shock features for three-dimensional (3D), two-dimensional (2D), and two-dimensional axisymmetric geometries. The nonequilibrium condensation, subsequent evaporation, and denucleation occurring across the shock are modeled through a set of user-defined scalar transport equations implemented within Ansys Fluent. The two-phase computational fluid dynamics (CFD) simulations are carried out in proximity to the critical point where real gas effects are relevant. The CO2 real gas properties are computed using an in-house Python code and integrated into the solver via user-defined functions as external look-up tables. This study provides valuable insights into the physical processes underlying two-phase shocks in CO2 flows and their sensitivity to geometric variations and thermodynamic conditions. The findings contribute to the development and modification of predictive models and optimized designs for systems involving two-phase CO2 flows. The results highlight the influence of geometry configurations and thermodynamic conditions on shock location and intensity, providing comparisons for shock waves occurring in two-phase flows and supercritical single-phase flows.

3DDA: A Novel Python Toolkit to Analyze 3D‐Dynamic Contact Angles from Molecular Dynamics Simulations

Obtaining fast and reliable contact angles in molecular dynamics simulations is crucial for screening surface wettability. Traditional methods rely on bidimensional density profile analysis to localize the liquid/vapor interface, which may not be optimal at the nanoscale, especially for nonsymmetrical droplets. Herein, 3DDA, a Python‐based code that uses the 3D droplet geometry to determine contact angles and analyze dynamic changes during spreading processes at the nanoscale, is presented. The 3D density profile of liquid molecules is enveloped using the Euclidean 3D convex hull algorithm to locate the contact line at the solid‐, liquid‐, and vapor‐phase boundaries. By fitting a general ellipsoidal function and optimizing it with the limited‐memory Broyden–Fletcher–Goldfarb–Shanno method, the best candidate function is analytically obtained, describing the interface. The angle derived from the ellipsoidal function and the solid boundary is used to gather a collection of contant angles, which are then fed into a kernel density estimator to determine the most probable angle along the contact line during the spreading process. This methodology is tested on systems such as liquid water and metals, demonstrating its efficacy and reliability. Herein, valuable insights are provided into the behavior of liquids, considering droplet asphericity induced by liquid/solid interactions.

Impact of spatially varying transport coefficients in EMC3-Eirene simulations of W7-X and assessment of drifts

Abstract Modelling the scrape-off layer of a stellarator is challenging due to the complex magnetic 3D geometry. The here presented study analyses simulations of the scrape-off layer (SOL) of the stellarator Wendelstein 7-X (W7-X) using spatially varying diffusion coefficients for the magnetic standard configuration, extending our previous study (Bold et al 2022 Nucl. Fusion 62 106011). Comparing the EMC3-Eirene simulations with experimental observations, an inconsistency between the strike-line width (SLW) and the upstream parameters was observed. While to match the experimental SLW a particle diffusion coefficient D ≈ 0.2 m2 s−1 is needed, D ≈ 1 m2 s−1 is needed to get experimental separatrix temperatures of 50 eV at the given experimental heating power. We asses the impact of physically motivated spatially varying transport coeffients. Agreement with experimental data can be improved, but various differences remain. We show that drifts are expected to help overcome the discrepancies and, thus, the development of SOL transport models including drifts is a necessary next step to study the SOL transport of the W7-X stellarator.