Detailed Geometry Research Articles

Modeling of Direct Energy Deposition for Wire Laser Additive Manufacturing

Abstract This work discusses the relevance of direct energy deposition (DED) and its classifications. Different types of DED processes are discussed, including their main parameters and issues. Two experiments were carried out, named single bead and rectangular prism, for the purpose of this research. These were built with a hot wire and laser DED system, where experimental thermal data were obtained, later geometric information was obtained via a 3D scan. Limitations of the equipment used as well as observed defects in the material deposition are discussed based on the experimental data. FEA models were developed to duplicate the experiments, which included a detailed geometry of the single bead. Two modifications to the bead geometry are presented and evaluated, where it is was concluded that a semi-circular bead approximation provides better results than if a rectangular one is assumed. This led to the definition of a thermal and structural equivalent model of the single bead, which was the basis for the numerical work of the rectangular prism. The results obtained for the latter show good agreement with the thermal results, although differences in the structural results are perceptible.

High-Detail 3D Reconstruction and Digital Strategies for the Enhancements of Archaeological Properties in Museums

In recent years, increasing attention has been directed toward the application of digitization through geomatic-based technologies for museum assets. These powerful tools have proven valuable in assisting museums in the dissemination of cultural heritage. Additionally, museums around the world are implementing strategies to improve the accessibility of their assets by involving the use of 3D digital reconstruction. The 3D high-precision survey is employed in several fields to scan objects with a geometrical accuracy up to the micrometer level. These technologies come into play when dealing with detailed surfaces and complex geometry, as often occurs with cultural heritage assets. This paper presents a set of experiences in high-precision 3D scanning and post-processing operations in the framework of a project at the Territory Museum of Riccione (Italy). The 3D data acquisition methodology conducted and digital operations are reported on for some of the scanned artifacts.

Does Subsurface Fault Geometry Affect Aleatory Variability in Modeled Strike-Slip Fault Behavior?

ABSTRACT The nonplanar geometry of faults influences their seismotectonic behavior, affecting the initiation, propagation, and termination of individual earthquakes as well as the stress–slip relationship and probability of multisegment rupture. Consequently, computer simulations that aim to resolve the earthquake rupture process and make predictions about a fault’s future behavior should incorporate nonplanar fault geometries. Although surface traces of faults can be mapped with high accuracy, a key challenge is to define a fault’s detailed subsurface geometry due to a general lack of data. This raises the question of which geometry to use. Does it matter which subsurface geometry is utilized in earthquake rupture simulations, as long as at least the fault trace is considered? How different is the simulated fault behavior for faults that share the same surface trace but different subsurface geometries? Using the physics-based earthquake-cycle simulator MCQsim, we generate seismic catalogs for 100 × 20 km strike-slip faults, assuming variations in fault surface trace, subsurface geometry, and strength distribution. We investigate how the long-term fault behavior—in the form of magnitude–frequency distribution, earthquake interevent time, and maximum earthquake size—is affected by fault geometry and strength distribution. We find that the simulated behavior of strike-slip faults with identical fault traces is interchangeable—even if their subsurface fault geometries differ. Implementing the fault trace constrains possible fault geometries to a level that makes the long-term behavior indistinguishable—at least for strike-slip faults with “known” strength distribution and length-to-width aspect ratios that are equal or larger than what we used here. The fault trace can provide a satisfactory representation of subsurface geometry for assessing long-term fault behavior.

Stokes flows in a two-dimensional bifurcation.

The flow network model is an established approach to approximate pressure-flow relationships in a bifurcating network, and has been widely used in many contexts. Existing models typically assume unidirectional flow and exploit Poiseuille's law, and thus neglect the impact of bifurcation geometry and finite-sized objects on the flow. We determine the impact of bifurcation geometry and objects by computing Stokes flows in a two-dimensional (2D) bifurcation using the Lightning-AAA Rational Stokes algorithm, a novel mesh-free algorithm for solving 2D Stokes flow problems utilizing an applied complex analysis approach based on rational approximation of the Goursat functions. We compute the flow conductances of bifurcations with different channel widths, bifurcation angles, curved boundary geometries and fixed circular objects. We quantify the difference between the computed conductances and their Poiseuille law approximations to demonstrate the importance of incorporating detailed bifurcation geometry into existing flow network models. We parametrize the flow conductances of 2D bifurcation as functions of the dimensionless parameters of bifurcation geometry and a fixed object using a machine learning approach, which is simple to use and provides more accurate approximations than Poiseuille's law. Finally, the details of the 2D Stokes flows in bifurcations are presented.

Impact of sub-basalt thrust systems on the Faroe continental shelf for the late Paleoproterozoic–Cenozoic tectonic evolution of the margin.

Background The Faroe margin in the northeastern Atlantic is segmented by margin-orthogonal, WNW–ESE-striking lineaments extending several hundred kilometers out to the continent–ocean transition. Despite several earlier studies speculating that these features are the product of reactivation of pre-Cenozoic basement-seated structures at depth, the thick Cenozoic volcano-sedimentary sequences deposited along the margin mask the underburden, thus rendering the identification and interpretation of such structures and resolving the pre-Cenozoic history of the area challenging. The present study documents for the first time the existence of margin-orthogonal basement-seated thrust systems and describes their detailed geometry, kinematics, and tectonic evolution. Methods We interpreted basement-seated tectonic structures on seismic reflection data from TGS on the Faroe Platform and the Wyville–Thomson and Munkagrunnur ridges using the newly established seismic facies of major thrust systems. Results The data show that the Wyville–Thomson Ridge, Munkagrunnur Ridge, and Faroe Platform are cored by WNW–ESE-striking thrust systems hundreds of kilometers long and 30–50 km wide, showing dominantly top-SSW kinematics. The thrusts were reworked into NE–SW-striking folds during the Caledonian Orogeny and controlled the formation of Caledonian thrusts, which in turn controlled the formation of post-Caledonian faults. The pre-Caledonian nature of the WNW–ESE-striking thrusts and their geometry and kinematics suggest a relationship with late Paleoproterozoic Laxfordian shear zones onshore northern Scotland and the continuation of the coeval Nagssugtoqidian Orogen in southeastern Greenland, the Ammassalik Belt. The thrust systems also align with the Tornquist Zone in eastern Europe and the North Sea, thus suggesting either that they controlled the formation of the Tornquist Zone or a possibly much longer (Paleoproterozoic?) tectonic history for the Tornquist Zone. Conclusions The Faroe Island margin is crosscut by late Paleoproterozoic Laxfordian–Nagssugtoqidian thrust systems, which controlled further tectonic development of the margin.

Quantification of Nearshore Sandbar Seasonal Evolution Based on Drone Pseudo-Bathymetry Time-Lapse Data

Nearshore sandbars are dynamic features that characterize shallow morphobathymetry and vary over a wide range of geometries and temporal lifespans. Nearshore sandbars influence beach geometry by altering the energy of incoming waves; thus, monitoring the evolution of sandbars is a fundamental approach in effective coastal planning. Due to several natural and technical limitations related to shallow seafloor mapping, there is a significant gap in the availability of high-resolution, shallow bathymetric data for monitoring the dynamic behaviour of nearshore sandbars effectively. This study introduces a novel image-processing technique that produces time series of pseudo-bathymetric data by utilizing multi-temporal (monthly) drone imagery, and it provides an assessment of local morphodynamics at a sandy beach in the southeast Mediterranean. The technique is called standardized-ratio bathymetric index (SRBI), and it transforms natural-colour drone imagery to pseudo-bathymetric data by applying an empirical formula used for satellite-derived bathymetry. This technique correlates well with laser altimetry depth measurements; however, it does not require in situ depth data for implementation. The resulting pseudo-bathymetric data allows for extracting cross-shore profiles and delineating the sandbar crest with 4 m horizontal accuracy. Stacking of temporal profiles allowed for the quantification of the sandbar’s crest and trough changes at different alongshore sections. The main findings suggest that the nearshore crescentic sandbar at Episkopi Beach (north Crete) shows strong seasonality regarding net offshore migration that is promoted by enhanced wave action during winter months. In addition, the crescentic sandbar is susceptible to morphology arrestment during prolonged weeks of low wave action. The average migration rate during winter is 10 m.month−1, with some sections exhibiting a maximum of 60 m.month−1. This study (a) offers a novel remote-sensing approach, suitable for nearshore seafloor monitoring with low computational complexity, (b) reveals sandbar geometry and temporal change in superior detail compared to other observational methods, and (c) advances knowledge about nearshore sandbar monitoring in the Mediterranean region.

Double-Pair Double-Difference Relocation Improves Depth Precision and Highlights Detailed 3D Fault Geometry for Induced Seismicity in Alberta, Canada

Abstract Precise earthquake locations with well-constrained uncertainties can improve our understanding of faulting. Double-difference relocation methods, particularly event-pair double-difference relocations, are well established and have been applied to large earthquake catalogs to provide fault geometries. Previous adaptations of the event-pair double-difference method include data space extensions to use additional information from station pairs, referred to as double-pair double-difference relocation. We apply double-pair double-difference relocation to data from a dense network of borehole geophones for induced seismicity monitoring. This experiment was acquired in an area with strong lithological variation and sharp velocity contrasts, and most previous studies using this dataset are subject to poorly constrained focal depths. We compare the double-pair double-difference to event-pair double-difference relocations and study the effectiveness and uncertainties of both methods. Although double-pair double-difference relocation does not improve absolute locations, substantially improved relative locations and reduced uncertainties are obtained. The method reduces the impact of path effects in the source region, which is essential for applications where reservoir units in the source region can exhibit strong velocity contrasts, anisotropy, and fractures. From the improved relocation, we produce a detailed 3D fault interpretation of the dataset that constrains the geological interpretation. The improved catalog shows excellent depth constraints with seismicity that is restricted to specific geological units. We interpret that seismicity activated pre-existing faults in the reservoir layer and adjacent units. Notably, the results show no evidence of induced seismicity activating basement structures.

Damage assessments of designed sandwich cores against an idealized impact loading of ship accidents: experiment-based validated nonlinear finite element approach

PurposeThe high rate of ship casualties demands serious attention. Earlier studies have yet to integrate several scenarios in an experiment. This research aims to determine damage to a ship's hull that is assumed to have been stranded due to being hit by a rock under the sea.Design/methodology/approachThis scenario was analyzed by conducting a penetration test simulation using the ANSYS LS DYNA. Modeling was carried out with core, speed, indenter shape and indenter angle variations. The test is carried out by moving the indenter against the plate until the plate is damaged.FindingsThe results of this research show that the round indenter gave the best results in testing, while changes in speed below 1.5 m/s did not significantly affect the results. The contact angle of the indenter to the panel is directly proportional to the damage to the panel, and core X has the best results in the test.Originality/valueThis work presented several proposed eccentric sandwich panels to calculate their performance against impact loading. The detailed geometry for future reference is presented, while the indenter-panel interactions are validated based on experimental work.

Dynamic effects on different rotor profiles for eVTOL applications

Electric Vertical Take-Off and Landing (eVTOL) aircraft, commonly known as eVTOLs, represent a recent and promising concept for enhancing urban mobility, particularly in densely populated areas. However, the development of eVTOLs has been accompanied by various technical and technological challenges, primarily due to the rapid introduction of new concepts. Among these challenges, this study focuses on investigating the dynamic effects on rotor blades designed for such aircraft. Rotor blades, being subject to rotational dynamic effects, exhibit behavior distinct from components lacking angular velocity. Therefore, they are of particular interest for dynamic analysis. The study begins by defining a simplified geometry representative of a rotor blade, which serves as the basis for applying finite element methods using Ansys software. This approach enables the observation of the system's response to rotation. Following, more detailed geometries representing rotors are modelled, respectively in NACA 0012, VR 08 and VR 12 airfoils, which are then subject to the rotational effects numerical calculations. The results obtained for the different aerodynamic profiles are then compared, presenting the differences caused by the different geometries and mass distributions implied. The results obtained from the analysis highlight the significant impact of angular velocity on the fundamental frequencies and modes of the dynamic system. These findings suggest the presence of effects such as spin softening and strain stiffening, along with identifying important frequency coupling points.

Integrating deep learning and machine learning for improved CKD-related cortical bone assessment in HRpQCT images: A pilot study.

High resolution peripheral quantitative computed tomography (HRpQCT) offers detailed bone geometry and microarchitecture assessment, including cortical porosity, but assessing chronic kidney disease (CKD) bone images remains challenging. This proof-of-concept study merges deep learning and machine learning to 1) improve automatic segmentation, particularly in cases with severe cortical porosity and trabeculated endosteal surfaces, and 2) maximize image information using machine learning feature extraction to classify CKD-related skeletal abnormalities, surpassing conventional DXA and CT measures. We included 30 individuals (20 non-CKD, 10 stage 3 to 5D CKD) who underwent HRpQCT of the distal and diaphyseal radius and tibia and contributed data to develop and validate four different AI models for each anatomical site. Manually annotated cortical bone was used to train each segmentation deep-learning model. Textural features were extracted via Gray-Level Co-occurrence Matrix (GLCM) and classified as CKD or non-CKD using XGBoost with each segmentation model. For comparison, manufacturer-supplied segmentation was used to extract cortical geometry, microarchitecture, and finite element analysis (FEA) outcomes. Model performance was confirmed using the test dataset and a separate independent validation cohort which included HRpQCT imaging from 42 additional individuals (18 non-CKD, 24 CKD stage 5D). For segmentation, the diaphyseal location showed strong performance on test datasets, with Mean IoUs of 0.96 and 0.95, and accuracies of 0.97 for both radius and tibia sites in CKD. Model 4 developed from the diaphyseal tibia region excelled in classifying test and independent validation datasets, achieving F1 scores of 0.99 and 0.96, AUCs of 0.99 and 0.94, sensitivities of 0.99, and specificities of 0.99 and 0.92. No single parameter, including BMD and cortical porosity, among conventional CT outcomes consistently differentiated CKD from non-CKD across all anatomical sites. Integrating HRpQCT with deep and machine learning, this innovative approach enables precise automatic segmentation of severely deteriorated endocortical surfaces and enhances sensitivity to CKD-related cortical bone changes compared to standard DXA and HRpQCT outcomes.

Multi-level Partition of Unity on Differentiable Moving Particles

We introduce a differentiable moving particle representation based on the multi-level partition of unity (MPU) to represent dynamic implicit geometries. At the core of our representation are two groups of particles, named feature particles and sample particles, which can move in space and produce dynamic surfaces according to external velocity fields or optimization gradients. These two particle groups iteratively guide and correct each other by alternating their roles as inputs and outputs. Each feature particle carries a set of coefficients for a local quadratic patch. These particle patches are assembled with partition-of-unity weights to derive a continuous implicit global shape. Each sampling particle carries its position and orientation, serving as dense surface samples for optimization tasks. Based on these moving particles, we develop a fully differentiable framework to infer and evolve highly detailed implicit geometries, enhanced by a multi-level background grid for particle adaptivity, across different inverse tasks. We demonstrated the efficacy of our representation through various benchmark comparisons with state-of-the-art neural representations, achieving lower memory consumption, fewer training iterations, and orders of magnitude higher accuracy in handling topologically complex objects and dynamic tracking tasks.

GarVerseLOD: High-Fidelity 3D Garment Reconstruction from a Single In-the-Wild Image using a Dataset with Levels of Details

Neural implicit functions have brought impressive advances to the state-of-the-art of clothed human digitization from multiple or even single images. However, despite the progress, current arts still have difficulty generalizing to unseen images with complex cloth deformation and body poses. In this work, we present GarVerseLOD, a new dataset and framework that paves the way to achieving unprecedented robustness in high-fidelity 3D garment reconstruction from a single unconstrained image. Inspired by the recent success of large generative models, we believe that one key to addressing the generalization challenge lies in the quantity and quality of 3D garment data. Towards this end, GarVerseLOD collects 6,000 high-quality cloth models with fine-grained geometry details manually created by professional artists. In addition to the scale of training data, we observe that having disentangled granularities of geometry can play an important role in boosting the generalization capability and inference accuracy of the learned model. We hence craft GarVerseLOD as a hierarchical dataset with levels of details (LOD) , spanning from detail-free stylized shape to pose-blended garment with pixel-aligned details. This allows us to make this highly under-constrained problem tractable by factorizing the inference into easier tasks, each narrowed down with smaller searching space. To ensure GarVerseLOD can generalize well to in-the-wild images, we propose a novel labeling paradigm based on conditional diffusion models to generate extensive paired images for each garment model with high photorealism. We evaluate our method on a massive amount of in-the-wild images. Experimental results demonstrate that GarVerseLOD can generate standalone garment pieces with significantly better quality than prior approaches while being robust against a large variation of pose, illumination, occlusion, and deformation. Code and dataset are available at garverselod.github.io.

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.

Microseismicity and fault structure in the Daliangshan block within the Southeastern Tibetan Plateau

The Daliangshan block is located between the Tibetan Plateau and the South China block and has accommodated several M > 6.5 damaging earthquakes in the past ∼600 years, as well as intense tectonic deformation and complex fault structures. In this study, we analyze more than two years of continuous seismic data recorded by a recently deployed dense seismic array. We used a recently developed machine learning-based earthquake location workflow (ESPRH) to construct a high-precision earthquake catalog for the region and obtained 3539 earthquakes, which is approximately three times as many as the National Earthquake Data Center (NEDC) catalog contains. The seismicity distribution not only confirms the nature of the faults marked on the map but also delineates the detailed geometry of the unmapped faults, including the en échelon faults at the northern end of the Zemuhe Fault and the “V”-shaped conjugate fault within the Mabian Fault Zone. The Zemuhe Fault and Lianfeng Fault are prone to hosting large earthquakes according to the derived low b-value. The western side of the Daliangshan block is dominated by strike-slip faults. Combining the fault geometry presented in this paper, we observed that the fault properties on the eastern side are complex. This tectonic phenomenon is attributed to the fact that during the lateral extrusion of the southeastern edge of the Chuandian fragments, the northeastern part of the Daliangshan block was squeezed by the South China block more strongly than its southwestern part. We provide the first precise earthquake catalog for Daliangshan block, which can be used as important seismological data for regional hazard assessment and research on the southeastern (SE) margin of the Tibetan Plateau.

The Concept of Levels of Detail for 3D Niche Models in CityGML

Abstract. Buddhist niches in grottoes can be represented in three-dimensional (3D) for their detailed geometries on surfaces by using triangular meshes generated from point clouds. However, not all applications require 3D models with high geometric detail. The mesh models of niches have drawbacks such as large data volumes, lack of semantic information, and absence of spatial relationships between structural components and members within niches. Those limitations make mesh models suitable only for visualization and challenging to use directly in tasks like spatial analysis, simulation experiments, mechanical analysis, and disease investigation. To address this problem, this study defines four Levels of Detail (LoDs) for Buddhist niches, ranging from LoD0 to LoD3, drawing on the concept of LoDs for urban buildings in CityGML 3.0. As the LoD level increases, 3D models contain more detailed geometries, including spatial points, bounding boxes, niche structural components, and component members. Those 3D models at different LoDs can represent niches with varying degrees of abstraction, making them suitable for different applications and guiding the production of standardized 3D semantic models. To validate the feasibility of the LoDs definition for Buddha niches, this paper reconstructs 3D models of niches at different LoDs based on high-precision mesh models. Finally, a comparison is made between the original mesh model and models at different LoDs in terms of data size and potential application scenarios.

Sorghum segmentation and leaf counting using in silico trained deep neural model

AbstractThis paper introduces a novel deep neural model for segmenting and tracking the number of leaves in sorghum plants in phenotyping facilities. Our algorithm inputs a sequence of images of a sorghum plant and outputs the segmented images and the number of leaves. The key novelty of our approach is in training the deep neural model. Manual annotations are tedious, and we have developed a procedural three‐dimensional (3D) sorghum model that provides detailed geometry and texture to generate photorealistic 3D models. The overall shape of the sorghum leaf geometry is determined by its skeleton, and it is detailed by a procedural model that varies its curvature, width, length, and overall shape. The color is determined by using a Monte Carlo path tracer. We mimic the illumination of the phenotyping facility and use reflectance and transmittance on sorghum surfaces to determine the color of the leaves. The 3D procedural model allows us to generate photorealistic and segmented images that we use to train a deep neural model. Our segmentation provides a mean intersection over union score of 0.51, resulting in leaf counting accuracy within the 90% confidence interval for the human counts.

Development of an EGSnrc multi-leaf collimator component module and treatment head model for a low-field MRI linear accelerator.

Monte Carlo (MC) modeling of MR-guided radiotherapy (MRgRT) treatment machines enables the characterization of photon/electron interactions in the presence of a magnetic field. The EGSnrc MC code system is a well-established system for radiation dose calculations. The multi-leaf collimator (MLC) component modules presently available within the EGSnrc MC code system do not include a model of the double-focused MLC available on a low-field (0.35T) MRI linear accelerator (MR linac). Here we developed and validated a new component module (CM) for the low-field MRgRT MLC using the EGSnrc/BEAMnrc/DOSXYZnrc code system. We performed detailed modeling of the treatment head and validated the model using measurements and calculations from the vendor-specific treatment planning system (TPS). The detailed geometry of the low-field MR linac MLC and other treatment head structures were modeled using BEAMnrc. Comparisons of DOSXYZnrc simulated dose against measurements and the low-field MR linac TPS for a variety of AAPM TG-53 task group report suggested square and shaped fields, as well as a step-and-shoot intensity-modulated radiotherapy (IMRT) plan, are presented. Our model agrees with both measured and TPS calculated data on average within 2%/2mm (dose/DTA) criterion for square field profiles. Output factors agreed within 1% for field sizes down to 2.49×2.49cm2 and within 2% of TPS data for the smallest field size of 0.83×0.83cm2. Shaped field and IMRT MC calculations agreed with measured and TPS data such that the gamma pass rates (3%/2mm) were 99.5% and (3%/3mm) 96.2%, respectively. We developed and validated an MLC CM (SYNCVRMLC) for the low-field MR linac using the EGSnrc MC code systems. This new CM will facilitate MC computation of fluence and dose distributions using BEAMnrc/DOSXYZnrc for patients treated on the low-field MR linac.

TriHuman: A Real-time and Controllable Tri-plane Representation for Detailed Human Geometry and Appearance Synthesis

Creating controllable, photorealistic, and geometrically detailed digital doubles of real humans solely from video data is a key challenge in Computer Graphics and Vision, especially when real-time performance is required. Recent methods attach a neural radiance field (NeRF) to an articulated structure, e.g., a body model or a skeleton, to map points into a pose canonical space while conditioning the NeRF on the skeletal pose. These approaches typically parameterize the neural field with a multi-layer perceptron (MLP) leading to a slow runtime. To address this drawback, we propose TriHuman a novel human-tailored, deformable, and efficient tri-plane representation, which achieves real-time performance, state-of-the-art pose-controllable geometry synthesis as well as photorealistic rendering quality. At the core, we non-rigidly warp global ray samples into our undeformed tri-plane texture space, which effectively addresses the problem of global points being mapped to the same tri-plane locations. We then show how such a tri-plane feature representation can be conditioned on the skeletal motion to account for dynamic appearance and geometry changes. Our results demonstrate a clear step toward higher quality in terms of geometry and appearance modeling of humans as well as runtime performance.

Guideline for Large-Scale Analysis of Centrifugal Blower Using Wall-Resolved Large Eddy Simulation

Abstract To reduce the number of prototypes during product design and accurately predict unsteady phenomena occurring at off-design points, a method for accurately predicting the performance of centrifugal blowers through numerical analysis is required. This article presents a guideline for accurately predicting the performance of centrifugal blowers using compressible flow analysis with large eddy simulation (LES). In LES analysis, it is important to have a grid resolution that resolves the minimum vortex scale near the wall (referred to as wall-resolved LES) and to consider detailed geometry such as the length of the suction pipe. The calculations in this study used a model blower, which is a scale model of a single-stage centrifugal blower for use in industrial plants. The model blower was experimentally measured for various parameters such as the blower pressure coefficient, the static-pressure-rise coefficients of the impeller and vane-less diffuser, the shaft power, and the pressure fluctuations at the inlet of the impeller and the inlet of the vane-less diffuser. The results of these measurements were compared with those obtained from the wall-resolved LES. The study confirmed that the accuracy of performance prediction can be improved to less than a 4.0% error in the blower pressure coefficient at both design and off-design operating points by resolving the minimum vortex scale with 14.6 billion-grid elements and considering the detailed geometry.

Computational modeling of predicting cerebrovascular injury in traumatic brain injury patients

Despite significant progress in understanding traumatic brain injury (TBI) and subsequent outcomes, predicting and preventing cerebrovascular injury (CVI) remains challenging. We introduces a novel computational modeling approach using advanced fluid-structure interaction (FSI) models, which incorporate high-resolution magnetic resonance imaging (MRI) data to analyze changes in intracranial pressure and blood flow in cerebral arteries post-TBI. We analyzed the mechanical fields between the PCOMA, ICA, PCA, and ACA areas by conducting a region of interest analysis in this research. The approach uniquely integrates detailed patient-specific brain vasculature geometries and dynamic boundary conditions to enhance predictive accuracy. The model indicates that peak pressure in the anterior cerebral artery (ACA) reaches 130 kPa within the first hour post-injury, with a 20% increase observed at 2 milliseconds. Wall shear stress (WSS) in the posterior communicating artery (PCOMA) reaches 140 kPa, which exceeds the typical physiological range of 10–20 kPa and may lead to endothelial damage. Regions such as the PCOMA and ACA are identified as particularly vulnerable to high mechanical stress and strain, which suggests the need for timely medical intervention to reduce the risk of CVI. Further validation with clinical data is required to improve the predictive accuracy of the model.