High-fidelity 3D Research Articles

Deep self-learning enables fast, high-fidelity isotropic resolution restoration for volumetric fluorescence microscopy

One intrinsic yet critical issue that troubles the field of fluorescence microscopy ever since its introduction is the unmatched resolution in the lateral and axial directions (i.e., resolution anisotropy), which severely deteriorates the quality, reconstruction, and analysis of 3D volume images. By leveraging the natural anisotropy, we present a deep self-learning method termed Self-Net that significantly improves the resolution of axial images by using the lateral images from the same raw dataset as rational targets. By incorporating unsupervised learning for realistic anisotropic degradation and supervised learning for high-fidelity isotropic recovery, our method can effectively suppress the hallucination with substantially enhanced image quality compared to previously reported methods. In the experiments, we show that Self-Net can reconstruct high-fidelity isotropic 3D images from organelle to tissue levels via raw images from various microscopy platforms, e.g., wide-field, laser-scanning, or super-resolution microscopy. For the first time, Self-Net enables isotropic whole-brain imaging at a voxel resolution of 0.2 × 0.2 × 0.2 μm3, which addresses the last-mile problem of data quality in single-neuron morphology visualization and reconstruction with minimal effort and cost. Overall, Self-Net is a promising approach to overcoming the inherent resolution anisotropy for all classes of 3D fluorescence microscopy.

Multiphysics Modeling of Solid Oxide Iron Air Battery with New Catalyst and Proton Conductor Oxide Support

Long-duration energy storage (LDES) (10+ hours) is widely regarded as an enabling technology to deepen the penetration of renewable energy into the commercial utility market. However, the current storage technologies cannot achieve LDES’s duration requirement at a competitive cost. Therefore, new LDES technologies are highly sought after in recent years. Solid oxide iron air battery is a newly emerging battery based on oxide-ion chemistry and stores energy in energy-dense solid iron. Our recent results have shown that the battery in a laboratory size (f1”) delivers 12.5-hour storage per cycle for 20 cycles with high energy capacity and round-trip efficiency. This presentation focuses on the description of a high-fidelity 2D axis symmetrical multi-physics model to simulate the performance of a solid oxide iron air battery. The model battery system consists of an anode-supported solid oxide cell and energy storage unit (ESU) of iron bed with a proton conducting oxide BaZr0.4Ce0.4Y0.1Yb0.1O3 (BZC4YYb) based support and iridium as a catalyst. The Multiphysics model encompasses charge transfer, mass transport, and chemical redox kinetic cycle occurring across all components of the battery and is validated with experimental results. The kinetic JMA (Johnson-Mehl-Avrami) model is used for describing the oxidation and reduction kinetics of Fe-BZC4YYb-IrO2 ESU. The motivation for combing the Ir catalyst with proton conductor oxide support in ESU is to boost the sluggish FeOx reduction kinetics. Compared to the baseline ESU, i.e. Fe2O3/ZrO2, the newly developed BZC4YYb-IrO2 shows great catalytic activity toward FeOx reduction, thus allowing SOIAB to operate at 500-550oC with excellent capacity, stability, and high round trip efficiency. The presentation will also show the experimental data of improved reduction kinetic rate of Fe-BZC4YYb-IrO2 over the baseline Fe2O3/ZrO2.

ImplicitPCA: Implicitly-proxied parametric encoding for collision-aware garment reconstruction

The emerging remote collaboration in a virtual environment calls for quickly generating high-fidelity 3D humans with cloth from a single image. To estimate clothing geometry and topology, parametric models are widely used but often lack details. Alternative approaches based on implicit functions can generate accurate details but are limited to closed surfaces and may not produce physically correct reconstructions, such as collision-free human avatars. To solve these problems, we present ImplicitPCA, a framework for high-fidelity single-view garment reconstruction that bridges the good ends of explicit and implicit representations. The key is a parametric SDF network that closely couples parametric encoding with implicit functions and thus enjoys the fine details brought by implicit reconstruction while maintaining correct topology with open surfaces. We further introduce a collision-aware regression network to ensure the physical correctness of cloth and human. During inference, an iterative routine is applied to an input image with 2D garment landmarks to obtain optimal parameters by aligning the cloth mesh projection with the 2D landmarks and fitting the parametric implicit fields with the reconstructed cloth SDF. The experiments on the public dataset and in-the-wild images demonstrate that our result outperforms the prior works, reconstructing detailed, topology-correct 3D garments while avoiding garment-body collisions.

Additive manufacturing of patient-specific high-fidelity and thickness-controlled cerebral aneurysm geometries

Additive Manufacturing (AM) offers unique advantages in rapidly fabricating highly complex and highly customized geometries; which suits very well for patient-specific medical applications. AM has been explored for a wide range of medical needs such as prosthetics, implants, surgical guides, and medical education. In particular, there is a growing need to leverage AM processing for achieving high-fidelity 3D biomimicry models to better understand critical physiological responses for clinical needs. In this study, a new design-manufacturing-evaluation workflow is presented and validated to manufacture complex optically clear and compliant 3D geometries that account for physiologically relevant conditions (i.e., 400 µm wall thickness at 100 mmHg) through an integrated AM (Vat Photopolymerization) and silicone molding technique. This study aims to establish the workflow by developing high-fidelity 3D intracranial aneurysm (IA) geometries using patient-specific data. The digital light processing (DLP) method was employed to create thin-walled shells for silicone molding. The samples created were successful in reproducing compliant, optically-clear aneurysms that can be used in in vitro studies. Findings from this study can be used to better understand the long-term growth effects of aneurysms, the causes for their sudden ruptures, and help develop potential prevention methods and treatments.

TED-Face: Texture-Enhanced Deep Face Reconstruction in the Wild.

We present TED-Face, a new method for recovering high-fidelity 3D facial geometry and appearance with enhanced textures from single-view images. While vision-based face reconstruction has received intensive research in the past decades due to its broad applications, it remains a challenging problem because human eyes are particularly sensitive to numerically minute yet perceptually significant details. Previous methods that seek to minimize reconstruction errors within a low-dimensional face space can suffer from this issue and generate close yet low-fidelity approximations. The loss of high-frequency texture details is a key factor in their process, which we propose to address by learning to recover both dense radiance residuals and sparse facial texture features from a single image, in addition to the variables solved by previous work-shape, appearance, illumination, and camera. We integrate the estimation of all these factors in a single unified deep neural network and train it on several popular face reconstruction datasets. We also introduce two new metrics, visual fidelity (VIF) and structural similarity (SSIM), to compensate for the fact that reconstruction error is not a consistent perceptual metric of quality. On the popular FaceWarehouse facial reconstruction benchmark, our proposed system achieves a VIF score of 0.4802 and an SSIM score of 0.9622, improving over the state-of-the-art Deep3D method by 6.69% and 0.86%, respectively. On the widely used LS3D-300W dataset, we obtain a VIF score of 0.3922 and an SSIM score of 0.9079 for indoor images, and the scores for outdoor images are 0.4100 and 0.9160, respectively, which also represent an improvement over those of Deep3D. These results show that our method is able to recover visually more realistic facial appearance details compared with previous methods.

Illumination-guided inverse rendering benchmark: Learning real objects with few cameras

The realm of 3D computer vision and graphics has experienced exponential growth recently, enabling the creation of realistic virtual environments and digital representations of real-world objects. Central to this progression are 3D reconstruction methods that facilitate the virtualization of shape, color, and surface details of real objects. Current methods predominantly employ neural scene representations, which despite their efficacy, grapple with limitations such as necessitating a high number of captured images and the complexity of transforming these representations into explicit geometric forms.An alternative strategy that has gained traction is the deployment of methods such as physically-based differentiable rendering (PBDR) and inverse rendering. These approaches require fewer viewpoints, yield explicit format results, and ensure a smoother transition to other representation methods. To meaningfully assess the performance of different 3D reconstruction methods, it is imperative to utilize benchmark scenes for comparison.Despite the existence of standard objects and scenes within the literature, there is a noticeable deficiency in real-world benchmark data that concurrently captures camera, illumination, and scene parameters — all critical to high-fidelity 3D reconstructions using PBDR and inverse rendering-based methods. In this research, we introduce a methodology for capturing real-world scenes as virtual scenes, integrating illumination parameters alongside camera and scene parameters to enhance the veracity of virtual representations. In addition, we introduce a set of ten real-world scenes, along with their virtual counterparts, designed as benchmarks. These benchmarks encompass a fundamental variety of geometric constructs, including convex, concave, plain, and mixed surfaces. Additionally, we demonstrate the 3D reconstruction results of state-of-the-art 3D reconstruction methods employing PBDR in real-world scenes, using both established methodologies and our proposed one.

Beam based rotordynamics modelling for preloaded Hirth, Curvic and butt couplings

Gas turbine and other machinery rotating assemblies are frequently manufactured as multiple components for cost and component alignment reasons. Butt joint, Hirth, and Curvic coupling are widely used for this purpose. Localized joint flexibility in these preloaded couplings introduce non-beamlike behavior which affects rotordynamic critical speeds, imbalance response and stability, rendering a conventional beam model inadequate. 3D solid finite element models of the couplings with Greenwood Williamson (GW) asperity interface features provide accurate representations of the couplings, however computational costs are impractical for use in an industrial design setting, which are limited to beam element models. A novel modeling approach for the coupling is developed that derives equivalent beam element Young's modulus and shear form factor properties, that replicate the bending behavior of the high fidelity 3D solid models, including GW based interface asperity stiffness. The equivalent beam models for butt, Hirth and Curvic couplings are validated using measured natural frequencies as a benchmark, for a range of through-bolt preloads. The equivalent beam model and the 3D solid model in this correlation incorporate GW contact models derived with experimentally measured surface roughness parameters. An Ecoupling sensitivity study for GW surface roughness parameters was conducted and showed a significant level of sensitivity. The effect of the coupling on an industrial class rotor's critical speed is included to illustrate usage of the approach.

Visible light photocrosslinking of sugar beet pectin for 3D bioprinting applications

Herein, we report the hydrogelation of sugar beet pectin (SBP) via visible light-mediated photocrosslinking and its applications in extrusion-based 3D bioprinting. Rapid hydrogelation (<15 s) was achieved by applying 405 nm visible light to an SBP solution in the presence of tris(bipyridine)ruthenium(II) chloride hexahydrate ([Ru(bpy)3]2+) and sodium persulfate (SPS). The mechanical properties of the hydrogel could be tuned by controlling the visible light irradiation time and concentrations of SBP, [Ru(bpy)3]2+, and SPS. High-fidelity 3D hydrogel constructs were fabricated by extruding inks containing 3.0 wt% SBP, 1.0 mM [Ru(bpy)3]2+, and 1.0 mM SPS. Human hepatoblastoma (HepG2) cells encapsulated in SBP hydrogels remained viable and metabolically active after 14 d of culture. Overall, this study demonstrates the feasibility of applying SBP and a visible light-mediated photocrosslinking system to the 3D bioprinting of cell-laden constructs for tissue engineering applications.

A teaching video for abdominal myomectomy with Maylard incision

Myomectomy is a common surgical procedure performed by Obstetricians and Gynecologists on women during their reproductive years. Surgical management can be accomplished hysteroscopically, laparoscopically, or abdominally depending on the size and location of the uterine fibroid.Large and/or multiple fibroids usually require an open approach. Transverse skin incisions are associated with less pain, quicker recovery, decreased hernia formation, and improved cosmesis. A Maylard incision allows for increased access to the pelvic sidewalls and can eliminate the need for a midline incision in patients with large pelvic pathology.There are no comprehensive, reliable educational videos for teaching this procedure. Curated instructional videos for surgical procedures can teach key surgical steps without needing to enter the operating room.This video is an excerpt from a full length teaching video demonstrating the key steps important for performing an abdominal myomectomy through a Maylard incision including patient positioning, relevant anatomy, and surgical technique. This video combines real-time operating room footage with high fidelity 3D animation.

High-fidelity Modelling Approach for Investigating the Tightening Torque Effect in Thread Connection

Accurate replication of the torque-tightening effect is important when simulating the mechanical behavior of composite joints using the finite element (FE) approach. In this study, a parametric mesh construction strategy was developed to automatically build a high-fidelity 3D FE model with lift angle to simulate bolt thread interactions. An experimental approach was proposed to calibrate the FE model. The relation between tightening torque and the axial force was studied by the FE method, experiment, and engineering algorithm. Results showed that traditional empirical formula overestimate the clamping force. By using the proposed model to calculate the axial force of the bolt, the accuracy of the axial force can be significantly improved when analyzing the composite bolted joints. Finally, parametric simulations were performed to investigate the influence of coefficients of friction at thread interfaces and the nut/washer interfaces. The pre-tightening force was significantly influenced by the frictional coefficients at these interfaces. Results indicated that increasing the coefficient of friction (CoF) at the nut/washer interface from 0.1 to 0.2 can lead up to 28% reduction in the clamping force, while this increase was 23% for the threads interfaces.

Thermal performance and SVM-based regression of natural convection in a 3D cavity filled with nanofluids as two phase mixture under combined effects of magnetic field and inner conductive hollow rotating conic object

In this study, a conductive hollow rotating conic object (H-RCO) is developed for convection control and thermal management in a 3D partially heated enclosure under uniform magnetic field with nanofluid considering two phase mixture formulation. Analysis is conducted for different parameters of interest as: Rayleigh number (Ra between 104 and 106), angular rotational speed of the H-RCO (Ω between −60 and 60), Hartmann number (Ha between 0 and 50), expansion ratio (r1 between 1.1 and 2.5) and conductivity ratio (KR between 0.01 and 50). The rotational speed and expansion ratio of the object contributes significantly to the overall performance improvements. At the highest speed of the H-RCO, the average Nusselt number (Nu) rises up to 38% when compared to cases of non-rotating object. When object with highest expansion ratio is used at rotational speed of Ω=−40, the average Nu rises by about 36%. The impacts of using magnetic field on the reduction of convective effects are stronger when rotations are active while up to 69% reduction of average Nu is seen at the highest strength. Thermal conductivity of the object at higher speeds contributes slightly to the overall heat transfer. Support vector machine based regression model is used for thermal performance predictions while model with third order polynomial kernel gives the best results as compared to high fidelity 3D computational results.

Expanding Access to 3D Technology in Plastic Surgery of the Breast: Validation of the Iphone X 3D Camera Against the Vectra M1

Abstract Goals/Purpose The iPhone X is the first mainstream smartphone to be released that contains a high-fidelity 3D scanner and is already in the hands of tens of millions of individuals in the United States. At the present time, the vast majority of 3D analysis of the breast necessitates ownership of expensive cost-prohibitive camera and software packages. The most popular of these is the Canfield Vectra M1 system. In an effort to expand access to 3D technology in plastic surgery of the breast, the purpose of this study was to compare the accuracy of the iPhone X 3D scanner against the Canfield Vectra M1 in obtaining 3D scans of the breast. Methods/Technique Twenty breasts (n=20) in patients undergoing breast reconstructive surgery were 3D photographed using both the iPhone X and the Canfield Vectra M1. The accuracy of the iPhone 3D image was compared with the Vectra image using color map analysis and surface distances across the 3D model between key anatomical landmarks. These included distances between sternal notch to nipple (SN-N), mid-chest to nipple (M-N), nipple to mid-inframammary fold (N-IMF), and inframammary fold width (IMF). Results/Complications When comparing absolute differences in distances between key anatomical landmarks, the average discrepancy in measurements between iPhone and Vectra image pairs were the following: SN-N: 0.84mm, M-N: 0.70mm, N-IMF 0.81mm, and IMF 0.91mm. Colormap analysis demonstrated an average root mean square error of 1.85mm, with a mean of 0.57mm and standard deviation of ±1.88mm. Conclusion The iPhone X is capable of capturing 3D photographs of the breast with an average discrepancy of approximately 1.0 to 2.0 millimeters as determined by landmark to landmark surface distance analysis and color map analysis. These data demonstrate that the 3D scans obtained with the iPhone may be useful for determining nipple position, measuring the breast footprint, choosing implants, and performing other functions using 3D technology are typically performed using the more expensive systems. These are early data and additional studies should compare volumetric analysis and clinical outcomes between using the iPhone and more established 3D technology systems in breast reconstructive surgery.

In-House 3D Printing and Model Processing Technique for Creating High-Fidelity Transparent Craniofacial Models.

The use of high-fidelity stereolithographic models that accurately reflect patient-specific pathology has become commonplace in craniofacial surgery. Multiple studies have reported the use of commercially available three-dimensional (3D) printers that allow medical centers with limited resources to reconstruct 3D models comparable to industry-made counterparts. However, most models are printed using only a single filament, which portrays the surface craniofacial anatomy, but fails to highlight relevant intraosseous structures. This presents a significant limitation when used for preoperative planning and intraoperative guidance in surgical procedures requiring osteotomies, where knowledge of the precise location of critical structures is paramount to avoid injury. The authors report a novel technique for creating transparent 3D models of relevant intraosseous craniofacial anatomy at a cost that mitigates the financial burden of industrial 3D model or industrial 3D printer acquisition. Cases are presented to demonstrate the diverse applications of this technique, with accurate display of the tooth roots, the inferior alveolar nerve, and the optic nerve, to aid in preoperative planning of osteotomies. This technique enables production of low-cost, high-fidelity transparent 3D models with applications in preoperative planning for craniofacial surgery.

High resolution and fidelity 3D printing of Laponite and alginate ink hydrogels for tunable biomedical applications

The formulation of hydrogels that meet the necessary flow characteristics for their extrusion-based 3D printing while providing good printability, resolution, accuracy and stability, requires long development processes. This work presents the technological development of a hydrogel-based ink of Laponite and alginate and evaluates its printing capacity. As a novelty, this article reports a standardizable protocol to quantitatively define the best printing parameters for the development of novel inks, providing new printability evaluation parameters such as the Printing Accuracy Escalation Index. As a result, this research develops a printable Laponite-Alginate hydrogel that presents printability characteristics. This ink is employed for the reproducible manufacture of 3D printed scaffolds with versatile and complex straight or curved printing patterns for a better adaptation to different final applications. Obtained constructs prove to be stable over time thanks to the optimization of a curing process. In addition, the study of the swelling and degradation behavior of the Laponite and alginate 3D printed scaffolds in different culture media allows the prediction of their behavior in future in vitro or in vivo developments. Finally, this study demonstrates the absence of cytotoxicity of the printed formulations, hence, setting the stage for their use in the field of biomedicine.

Probability embedded failure prediction of unidirectional composites under biaxial loadings combining machine learning and micromechanical modelling

This study presents a data-driven, probability embedded approach for the failure prediction of IM7/8552 unidirectional carbon fibre reinforced polymer (CFRP) composite materials under biaxial stress states based on micromechanical modelling and artificial neural networks (ANNs). High-fidelity 3D representative volume element (RVE) finite element models were used for the generation of failure points. Fibre failure and the friction between fibres and matrix after fibre/matrix debonding were taken into consideration and implemented as VUMAT subroutines, respectively. Uncertainty quantification was conducted based on a coupled experimental–numerical approach and failure probabilities were inserted into the failure points to generate the database for the training of ANNs. A total of 15 biaxial stress combinations were considered for the generation of datasets. Two strategies were considered for the construction of form-free failure criteria based on the ANNs for regression and classification problems. It is found that for the regression problems, an ANN model with 2 hidden layers and 64 neurons can achieve a mean square error (MSE) of 0.027% and a mean absolute error (MAE) of 0.78%. For the classification problems, an ANN model with 3 hidden layers and 32 neurons, presents an excellent performance in the prediction with a probability of 98.1%. A good agreement was observed between the failure strength of composites under transverse and in-plane shear predicted by these ANNs and failure envelopes theoretically predicted by Tsai–Wu and Hashin failure criteria.

A novel diffraction contrast tomography (DCT) acquisition strategy for capturing the 3D crystallographic structure of pure titanium

The acquisition of high-fidelity 3D grain maps is essential for advancing our understanding of the micromechanical behavior of polycrystalline materials. Grain orientations, grain boundary misorientations, and grain shapes play a significant role in slip transfer mechanisms and grain growth phenomena. The past few years have seen considerable advances in the acquisition of high-reliability grain maps using laboratory-based Diffraction Contrast Tomography (LabDCT). Additionally, the microstructures of challenging sample geometries have become more accessible at the lab scale with recent developments in advanced Lab DCT acquisition strategies, such as helical phyllotaxis Lab DCT (HP-DCT). Unlike a conventional Lab DCT (C-DCT) scan, in which an elongated sample is scanned in multiple sections, a helical phyllotaxis motion is employed in an HP-DCT scan to illuminate the different parts of the sample in a single scan. This strategy can theoretically allow to scan and reconstruct challenging sample geometries, such as elongated and high aspect ratio samples, in a single process with fewer diffraction projections and reduced scan and analyses times. In this study, a detailed analysis of the grain maps for a pure Ti sample obtained from C-DCT and HP-DCT scan data is carried out. The DCT grain maps are compared with the surface grain maps obtained from ground-truth EBSD and SEM scans. Furthermore, the quality of grain reconstructions, grain orientations, grain boundary misorientations, grain shapes and morphology is quantitatively assessed, and the differences in accuracy of grain maps obtained from the conventional and helical phyllotaxis scans are highlighted. These results indicate that the grain reconstructions from HP-DCT scans have comparable grain fidelity to those obtained from C-DCT scan, with the conventional scans performing marginally better in terms of grain shape and orientation but at a higher time cost.

MAxSIM: Multi-angle-crossing structured illumination microscopy with height-controlled mirror for 3D topological mapping of live cells.

The plasma membrane sheet can fold into dynamic, three-dimensional (3D) structures that correlate with cell functionality. Mapping 3D plasma membrane topology in live cells can provide unprecedented insights into cell biology and signaling. Fluorescence light interference contrast (FLIC) microscopy and its derivatives (e.g., scanning angle interference microscopy) use axial interferometry to determine nanoscale axial locations of cell-surface proteins that are placed on a mirror along the optical axis. However, FLIC derivatives have limitations including low fidelity/poor axial localization and low time resolution due to slow mechanical control of incident angles, cell placement on a mirror, and an unoptimized data fitting algorithm, as well as diffraction-limited lateral imaging. To resolve these challenges, we developed multi-angle-crossing structured illumination microscopy (MAxSIM). MAxSIM generates multiple incident angles by optoelectronically creating diffraction patterns as in structured illumination microscopy to enable super-resolution lateral imaging. MAxSIM performance is enhanced with a custom-fabricated height-controlled mirror (HCM) placed at a fixed location from the bottom glass substrate, which significantly increases axial localization fidelity and optimizes the height reconstruction scheme. Use of HCM additionally enables cell placement on the glass side of the bottom substrate and flexible cell imaging from both the top and bottom of a cell. Our MAxSIM platform with an HCM and optimized reconstruction scheme thus offers high-fidelity nanoscale 3D topological mapping of cell plasma membranes and enables near-real-time (∼1 Hz) imaging of live cells and (∼20 Hz) 3D single molecule tracking. These innovations will significantly advance basic biological research by providing new and widely applicable tools that enable novel 3D measurements of cell-surface topologies that will reveal unprecedented mechanistic details of the control of cellular functions.

Beyond 3DMM: Learning to Capture High-Fidelity 3D Face Shape.

3D Morphable Model (3DMM) fitting has widely benefited face analysis due to its strong 3D priori. However, previous reconstructed 3D faces suffer from degraded visual verisimilitude due to the loss of fine-grained geometry, which is attributed to insufficient ground-truth 3D shapes, unreliable training strategies and limited representation power of 3DMM. To alleviate this issue, this paper proposes a complete solution to capture the personalized shape so that the reconstructed shape looks identical to the corresponding person. Specifically, given a 2D image as the input, we virtually render the image in several calibrated views to normalize pose variations while preserving the original image geometry. A many-to-one hourglass network serves as the encode-decoder to fuse multiview features and generate vertex displacements as the fine-grained geometry. Besides, the neural network is trained by directly optimizing the visual effect, where two 3D shapes are compared by measuring the similarity between the multiview images rendered from the shapes. Finally, we propose to generate the ground-truth 3D shapes by registering RGB-D images followed by pose and shape augmentation, providing sufficient data for network training. Experiments on several challenging protocols demonstrate the superior reconstruction accuracy of our proposal on the face shape.

Rapid Simulation of Unconventional Reservoirs by Multidomain Multiresolution Modeling Based on the Diffusive Time of Flight

Summary The fast marching method (FMM)-based rapid flow simulation has been shown to accelerate simulation efficiency by orders of magnitude by transforming 3D simulation to equivalent 1D simulation using the concept of the “diffusive time of flight” (DTOF). However, the 1D transformation does not directly apply to multiwell problems. In this paper, we propose a novel DTOF-based multidomain multiresolution discretization scheme to accelerate multiwell simulation of unconventional reservoirs. Our method formulates multiwell simulation problems based on the DTOF which displays the pressure front propagation in unconventional reservoirs. The DTOF contours are used to partition the reservoir into local and shared domains. A local domain is where the flow is dominated by a single well, and the shared domain is where the fluid flow is influenced by multiple wells. The DTOF contours expand independently in local domains and interfere in the shared domain. After the partitioning, each domain is discretized using a multiresolution scheme whereby the original 3D fine mesh is preserved near the wells to account for detailed physics including gravity, and the rest of the domain is discretized into 1D mesh based on the DTOF contours to alleviate the simulation workload. The power and efficacy of our approach are demonstrated using synthetic and field-scale simulation models with different degrees of geologic and well-completion complexity. The simulation results, number of active cells, and computation time for the proposed discretization scheme are compared with the original high-fidelity 3D model for each case. The results show that the proposed method is suitable for multiwell simulation problems in unconventional reservoirs and can accelerate flow simulations by orders of magnitude with minimal loss of accuracy. The novelty of this work is the creation of DTOF-derived multiresolution discretization with local and shared domains to simplify and accelerate the calculation of subsurface flow problems, especially in unconventional reservoirs. Our workflow can be easily interfaced with commercial simulators, making it suitable for large-scale field applications.

High-Fidelity 3D Stray Magnetic Field Mapping of Smartphones to Address Safety Considerations with Active Implantable Electronic Medical Devices.

Case reports indicate that magnets in smartphones could be a source of electromagnetic interference (EMI) for active implantable medical devices (AIMD), which could lead to device malfunction, compromising patient safety. Recognizing this challenge, we implemented a high-fidelity 3D magnetic field mapping (spatial resolution 1 mm) setup using a three-axis Hall probe and teslameter, controlled by a robot (COSI Measure). With this setup, we examined the stray magnetic field of an iPhone 13 Pro, iPhone 12, and MagSafe charger to identify sources of magnetic fields for the accurate risk assessment of potential interferences with AIMDs. Our measurements revealed that the stray fields of the annular array of magnets, the wide-angle camera, and the speaker of the smartphones exceeded the 1 mT limit defined by ISO 14117:2019. Our data-driven safety recommendation is that an iPhone 13 Pro should be kept at least 25 mm away from an AIMD to protect it from unwanted EMI interactions. Our study addresses safety concerns due to potential device-device interactions between smartphones and AIMDs and will help to define data-driven safety guidelines. We encourage vendors of electronic consumer products (ECP) to provide information on the magnetic fields of their products and advocate for the inclusion of smartphones in the risk assessment of EMI with AIMDs.