Search NASA⌕ Search

SEARCH · Search NASA

Results for “Fracture network”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 271 records · Page 15
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

A chain stretch-based gradient-enhanced model for damage and fracture in elastomers

Similar to quasi-brittle materials, it has been recently shown that elastomers can exhibit a macroscopically diffuse damage zone that accompanies the fracture process. In this study, we introduce a stretch-based gradient-enhanced damage (GED) model that allows the fracture to localize and also captures the development of a physically diffuse damage zone. This capability contrasts with the paradigm of the phase field method for fracture, where a sharp crack is numerically approximated in a diffuse manner. Capturing fracture localization and diffuse damage in our approach is achieved by considering nonlocal effects that encompass network topology, heterogeneity, and imperfections. These considerations motivate the use of a statistical damage function dependent upon the nonlocal deformation state. From this model, fracture toughness is realized as an output. While GED models have been classically utilized for damage modeling of structural engineering materials (e.g., concrete), they face challenges when trying to capture the cascade from damage to fracture, often leading to damage zone broadening (de Borst and Verhoosel, 2016). This deficiency contributed to the popularity of the phase-field method over the GED model for elastomers and other quasi-brittle materials. Other groups have proceeded with damage-based GED formulations that prove identical to the phase-field method (Lorentz et al., 2012), but these inherit the aforementioned limitations. To address this issue in a thermodynamically consistent framework, we implement two modeling features (a nonlocal driving force bound and a simple relaxation function) specifically designed to capture the evolution of a physically meaningful damage field and the simultaneous localization of fracture, thereby overcoming a longstanding obstacle in the development of these nonlocal strain- or stretch-based approaches. Here, we discuss several numerical examples to understand the features of the approach at the limit of incompressibility, and compare them to the phase-field method as a benchmark for the macroscopic response and fracture energy predictions.

Elastomers↗
DOE OSTI2021

Nanoscale Interfacial Smoothing and Dissolution during Unconventional Reservoir Stimulation Implications for Hydrocarbon Mobilization and Transport

Hydraulic fracturing of low-permeability rocks significantly enhances hydrocarbon production from unconventional reservoirs. However, fluid transport through low-permeability rocks, and the influence of geochemical transformations on pore networks, are poorly constrained. Mineral reactivity during interactions with injected water may alter the physical nature of the rock, which may affect hydrocarbon mobility. To assess alteration to the rock, we previously conducted a hydrothermal experiment that reacted cubed rock samples (1 cm3) with synthetic hydraulic fracturing fluid to simulate physicochemical reactivity during hydraulic fracturing. We analyzed unreacted and reacted rocks by small-angle neutron scattering and high-pressure mercury intrusion to determine how nanoscale (~1-1400 nm) pore networks of unconventional reservoir rocks are influenced by reaction with hydraulic fracturing injectates. We show that fluid-rock interactions have a two-fold influence on hydrocarbon recovery, promoting both hydrocarbon mobilization and transport. Pore-matrix interfaces smooth via removal of clay mineral surface asperities, reducing available surface area for hydrocarbon adsorption by ca. 12-75%. Additionally, hydraulic fracturing fluid-induced dissolution creates new pores with diameters ranging in size from 800-1400 nm, increasing the permeability of the rocks by a factor of 5-10. These two consequences of mineral dissolution act in concert to release hydrocarbons from the host rock and facilitate transport through the rock during unconventional reservoir production.

Herz-Thyhsen, Ryan↗
DOE OSTI2022

Accelerating Hydraulic Fracture Imaging by Deep Transfer Learning

Deep transfer learning has a great success story in computer vision (CV), natural language processing, and many other fields. In this communication, we are going to push forward the deep transfer learning to the hydraulic fracture imaging problem by proposing a two-step approach: 1) train a convolutional neural network (CNN) to reconstruct target geometries by a relatively large amount of approximated field patterns generated from a simplified model and 2) fine-tune the top layers of transferred CNN by a small amount of true field patterns generated through a full model. The advantages include the rapid generation of large amount of data through the simplified model and the high reconstruction accuracy through a careful design of the deep transfer learning. Here, the CNN trained through the deep transfer learning can accurately reconstruct the lateral extent and direction of fractures with unseen conductivity and white Gaussian noise, showing a notable acceleration/accuracy enhancement over the previous CNN trained by mixing a small number of true data into the approximated data as data augmentation.

02 PETROLEUM↗
DOE OSTI2024

Integrating the Design of Desalination Technologies into Produced Water Network Optimization

The oil and gas energy sector uses billions of gallons of water for hydraulic fracturing each year to extract oil and gas. The water injected into the ground for fracturing along with naturally occurring formation water from oil wells surfaces back in the form of produced water. Produced water can contain high concentrations of total dissolved solids and is unfit for reuse outside the oil and gas industry without desalination. In semi-arid shale plays, produced water desalination for beneficial reuse could play a crucial role in alleviating water shortages and addressing extreme drought conditions. In this poster we co-optimize the design and operation of desalination technologies along with operational decisions across produced water networks. A multi-period produced water network model with simplified split-fraction-based desalination nodes is developed. Rigorous steady-state desalination mathematical models based on mechanical vapor recompression are developed and embedded at the desalination sites in the network model. An optimal common design across all periods is ensured using global capacity constraints. The solution approach is demonstrated for multi-period planning problems on networks from the PARETO open-source library. Model formulation and challenges associated with scalability are discussed.

Naik, Sakshi↗
DOE OSTI2024

Integrating the Design of Desalination Technologies into Produced Water Network Optimization

The oil and gas energy sector uses billions of gallons of water for hydraulic fracturing each year to extract oil and gas. The water injected into the ground for fracturing along with naturally occur-ring formation water from the oil wells surfaces back in the form of produced water. Produced water can contain high concentrations of total dissolved solids and is unfit for reuse outside the oil and gas industry without desalination. In semi-arid shale plays, produced water desalination for beneficial reuse could play a crucial role in alleviating water shortages and addressing extreme drought conditions. In this paper we co-optimize the design and operation of desalination technologies along with operational decisions across produced water networks. A multi-period produced water network model with simplified split-fraction-based desalination nodes is developed. Rigorous steady-state desalination mathematical models based on mechanical vapor recompression are developed and embedded at the desalination sites in the network model. An optimal common design is ensured across all periods using global capacity constraints. The solution approach is demonstrated for multi-period planning problems on networks from the PARETO open-source library. Model formulation and challenges associated with scalability are discussed.

Naik, Sakshi↗
DOE OSTI2024

Image-Based Fracture Surface Defect Characterization Methods for Additively Manufactured Ti-6Al-4V Tested in Fatigue

Abstract Fatigue initiation in additively manufactured samples/parts often occurs at processed-induced defects such as lack-of-fusion (LoF), keyhole, or other morphological/microstructural defects that have unique characteristics and measurable qualities. Attempts at identifying and minimizing such defects have utilized optimized processing conditions along with in situ and ex situ characterization that includes metallography and/or X-ray computed tomography (XCT). This paper highlights the benefits of using fracture surface analyses to detect and quantify defects that may not be detected by metallography/XCT due to sectioning and resolution limits. In addition to using manual quantification of fatigue initiating LoF and keyhole defects on fracture surfaces, image-based machine learning using convolutional neural networks such as U-Net were also used to automate the process. Statistical analyses were used to identify the extreme cases of defects that initiated and accelerated fatigue and to model the distribution of defect size and shape characteristics to distinguish the type of defect. Initial results show agreement between trained machine learning models and ground truth data in defect segmentation, and the distributions of defect characteristics are distinguishable to particular process-induced defect types.

Materials Science↗
DOE OSTI2020

A machine learning approach to fracture mechanics problems

Analytical and empirical solutions to engineering problems are usually preferred because of their convenience in applications. However, they are not always accessible in complex problems. A new class of solutions, based on machine learning (ML) models such as regression trees and neural networks (NNs), are proposed and their feasibility and value are demonstrated through the analysis of fracture toughness measurements. It is found that both solutions based on regression trees and NNs can provide accurate results for the specific problem, but NN-based solutions outperform regression-tree-based solutions in terms of their simplicity. Here this example demonstrates that ML solutions are a major improvement over analytical and empirical solutions in terms of both reliable functionality and rapid deployment. When analytical solutions are not available, the use of ML solutions can overcome the limitations of empirical solutions and substantially change the way that engineering problems are solved.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Lattice modeling of excavation damage in argillaceous clay formations: Influence of deformation and strength anisotropy

This paper presents modeling of mechanical anisotropy in argillaceous rocks using an irregular lattice modeling approach, namely the rigid-body-spring network. To represent the mechanical anisotropy, new schemes are implemented in the modeling framework. The directionality of elastic deformation is resolved by modifying the element formulation with anisotropic elastic properties. The anisotropy of strength and failure characteristics is facilitated by adopting orientation-dependent failure criteria into the failure model. The verification of the improved modeling procedures is performed against theoretical model predictions for unconfined compression tests with various bedding orientations. Furthermore, excavation damage and fracturing processes in rock formations are simulated for different geomechanical configurations, such as rock anisotropy and tectonic heterogeneity. The simulated excavation damage characteristics are realistic and comparable with the actual field observation at a tunnel located in an argillaceous clay formation. The simulation results provide insights into the excavation damage zone phenomena with an explicit representation of fracturing processes.

58 GEOSCIENCES↗
DOE OSTI2024

Geophysical Signatures of Crack Network Coalescence in Rocks at Multiple Length Scales

The main goal of the research project was to identify the geophysical signatures of fracture growth in natural rocks by utilizing novel geophysical techniques. The research objectives were to (a) investigate the potential for geophysical methods to determine when cracks initiate, the types and locations of propagated cracks, and the coalescence of networks of cracks in natural rocks at multiple scales, (b) determine how damage at the microscale evolved into damage at the macroscale and then link the microscopic and macroscopic observations, (c) quantify crack coalescence in rocks under realistic stress conditions using coupled mechanical-geophysical-optical visualization, and (d) identify the precursors in geophysical signals to crack coalescence. The following research thrusts were explored to achieve the research objectives: (1) uniaxial compression testing of rock specimens with and without a set of pre‐existing flaws and (2) triaxial compression testing of natural rock specimens. These thrusts allowed for exploring fracturing in rocks under realistic in situ environments and at multiple scales. This project provided educational opportunities for nine graduate and undergraduate students and resulted in 27 peer-reviewed publications. This first research thrust focused on investigating the micromechanics of fractures in rocks through uniaxial compression testing combined with advanced geophysical and imaging techniques, specifically acoustic emission (AE) monitoring, ultrasonic imaging, and 2-dimensional Digital Image Correlation (2D-DIC). By examining damage processes under time-independent and time-dependent loading conditions, insights into damage localization, crack initiation, and fracturing mechanisms were gained. It was observed that the AE signals and the strain-based measurements directly reflect the state of damage in the rock specimen and could be used to identify the cracking levels, such as the crack initiation (CI) and crack damage (CD), and the mode of deformation. A novel calibration apparatus was developed to enhance the accuracy of AE sensors, allowing for the estimation of key parameters such as magnitude, source dimension, stress drop, and radiated seismic energy associated with the fractures. The findings highlighted significant variations in the temporal evolution of AE source parameters during the primary, secondary, and tertiary stages of creep, identifying tensile cracking as the primary deformation mode. The second research thrust focused on enhancing the understanding of fracturing processes in natural rocks through triaxial compression testing, real-time AE monitoring, and ultrasonic monitoring. We investigated the impact of various factors such as fracture propagation regimes, injection parameters, rock types, and pre-existing conditions on the hydraulic fracture (HF) behavior using scaled true-triaxially loaded specimens of Barre granite and Lyons sandstone. Custom sensor housing facilitated concurrent active and passive monitoring to analyze hydro-mechanical responses and microseismicity associated with different HF scenarios. A coupled investigation of passive microseismicity and active signal attributes permitted a detailed comprehension of the various HF processes (aseismic deformation, fracture initiation and propagation, fluid permeation, and leak-off) and their dependence on the specific rock type. The findings of this research demonstrated the effectiveness of AE monitoring techniques in providing valuable insights into the impact of various factors on the behavior and dynamics of HF processes. The advancements in monitoring techniques, offering a more thorough and precise approach, represent a significant step towards optimizing HF practices and ensuring sustainable resource extraction.

58 GEOSCIENCES↗
DOE OSTI2025

Mitigating Battery Cell Failure: Role of Ag‐Nanoparticle Fillers in Solid Electrolyte Dendrite Suppression

Abstract The development of solid‐state batteries (SSBs) with lithium Li metal anodes holds significant promise for enhancing the energy density and safety of next‐generation energy storage systems. However, their commercialization is hindered by challenges related to Li dendrite formation, which can lead to short circuits and battery failure. In this study, the role of Ag nanoparticles embedded within solid electrolytes (SE) is investigated in suppressing dendrite propagation. The results demonstrate that Ag nanoparticles effectively mitigate two key failure mechanisms: (1) dendrite growth within porous networks at low current densities and (2) stress intensification‐induced SE fracture at higher current densities (12 mA cm −2 ). Ex situ characterization using focused‐ion beam – scanning electron microscopy (FIB–SEM) and energy dispersive X‐ray spectroscopy (EDS,) reveals that Ag nanoparticles migrate alongside advancing Li dendrites, promoting homogeneous dendrite growth and reducing the likelihood of localized stress concentrations. Additionally, the incorporation of Ag nanoparticles is shown to facilitate a more uniform Li distribution toward the anode side, which can potentially enable the use of higher charging rates in SSBs. This study provides a new perspective on Li dendrite suppression and presents new opportunities for enhancing the performance and safety of SSBs.

Diallo, Mouhamad Said↗
DOE OSTI2025

High temperature tensile properties of H13 tool steel fabricated via binder jet additive manufacturing

H13 is one of the most used steels for hot work tooling applications like die casting. Binder jet additive manufacturing (BJAM) offers a cost-effective solution to fabricate tools with performance enhancing features like internal cooling channels. While full densification of BJAM H13 has been demonstrated in the literature, the room and high temperature tensile properties of the material, and the effect of post-process treatments like hot isostatic pressing (HIP), and heat treatment (HT) are unknown. Here we use HIP+HT to obtain a combination of ultimate tensile strength (UTS) and elongation to failure that is superior to H13 fabricated via laser powder bed fusion and wire arc additive manufacturing. HIP+HT BJAM H13 has a superior UTS (1836 MPa) compared with conventionally processed (1671 MPa) H13 but has lower plastic elongation. We report that as-sintered samples have an extensive network of continuous grain boundary (GB) carbides that contribute to brittle fracture. HIP+HT results in partial dissolution of GB carbides and formation of a fine martensitic microstructure resulting in superior tensile strength and elongation compared with as-sintered H13. We use thermo-kinetic calculations to rationalize the effects of HIP+HT on microstructure and properties.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Unraveling the Highly Plastic Behavior of ALD-Aluminum Oxide Encapsulations by Small-Scale Tensile Testing

We present a study directly measuring the electron-beam-induced plasticity of amorphous Al 2 O 3 coatings. Core–shell nanostructures are employed as small-scale model systems for two-dimensional coatings made by atomic layer deposition (ALD). Copper nanowires (NWs) are used as substrates for ALD deposition, representing a model system for interconnects commonly found in integrated circuits. Experiments are performed in situ in a transmission electron microscope (TEM) and further analyzed with electron energy loss spectroscopy (EELS). Our in situ TEM tensile experiments reveal the highly plastic behavior of the ALD shell, which withstands a maximum strain of 188%. Comparable samples under beam-off conditions show a brittle fracture, which underlines the effect of electron irradiation. The electron-beam-activated bond switching within the amorphous network enables compensation of the applied tensile strain, leading to viscous flow. By incorporating an intermediate nanocrystalline layer within the Al 2 O 3 shell, the plasticity is suppressed and brittle fracture occurs. This work directly demonstrates the tuning of mechanical properties in amorphous ALD structures through electron irradiation.

36 MATERIALS SCIENCE↗
DOE OSTI2022

In-situ imaging of flexure-induced fracture in tape-laminate composites using high-resolution X-ray computed tomography

Here, this study presents development of an in-situ flexure test for imaging progressive inter- and intralaminar fracture in tape-laminate composites using X-ray computed tomography (CT). The intent of this test is to provide detailed experimental observations of ply-level damage that can be used to validate existing, and develop new, progressive damage analysis (PDA) tools. The test consists of a vertically mounted specimen which is flexed using two eccentric compressive loads using an in-situ uniaxial load stage. The flexure specimen contains a starter notch mid-span which promotes initiation of composite failure within the X-ray field of view. Specimens with two different laminate stacking sequences were tested, imaged, analyzed. For a quasi-isotropic laminate with large angle changes between adjacent plies, there was near simultaneous growth of transverse cracks and delaminations below the midplane of the laminate. For a laminate with small angles between adjacent plies, there was extensive formation of transverse crack networks which penetrated the laminate thickness without delamination growth. In addition to imaging fracture, the X-ray CT data from both specimen types were used to quantify the variability in thickness and analyze the local orientation of individual plies. Overall, the proposed test and the image-data analysis methodology provided an important insight into the fracture processes in tape laminates and highlights the inherent ply-level geometrical variabilities that should be accounted for in PDA simulations.

42 ENGINEERING↗
DOE OSTI2021

Subsurface Characterization and Machine Learning Predictions at Brady Hot Springs: Preprint

Subsurface data analysis, reservoir modeling, and machine learning (ML) techniques have been applied to the Brady Hot Springs (BHS) geothermal field in Nevada, USA to further characterize the subsurface and assist with optimizing reservoir management. Hundreds of reservoir simulations have been conducted in TETRAD-G and CMG STARS to explore different injection and production fluid flow rates and allocations and to develop a training data set for ML. This process included simulating the historical injection and production since 1979 and prediction of future performance through 2040. ML networks were created and trained using TensorFlow based on multilayer perceptron (MLP), long short-term memory (LSTM), and convolutional neural network (CNN) architectures. These networks took as input selected flow rates, injection temperatures, and historical field operation data and produced estimates of future production temperatures. This approach was first successfully tested on a simplified single fracture doublet system, followed by the application to the BHS reservoir. Using an initial BHS dataset with 37 simulated scenarios, the trained and validated network predicted the production temperature for 6 production wells with the mean absolute percentage error of less than 8%. In a complementary analysis effort, the principal component analysis applied to 13 BHS geological parameters revealed that vertical fracture permeability shows the strongest correlation with fault density and fault intersection density. A new BHS reservoir model was developed considering the fault intersection density as proxy for permeability. This new reservoir model helps to explore under-exploited zones in the reservoir. A data gathering plan to obtain additional subsurface data was developed; it includes temperature surveying for three idle injection wells, at which the reservoir simulations indicate high bottom-hole temperatures. The collected data assist with calibrating the reservoir model and may lead to converting these wells to producers to access under-exploited zones in the reservoir. Data gathering activities are planned for the first quarter of 2021.

40 EE - Geothermal Technologies Office (EE-4G)↗
DOE OSTI2021

Quantitative microstructural investigation of 3D-printed and cast cement pastes using micro-computed tomography and image analysis

Highlights: • Processing alters the microstructure in 3D-printed lamellar cubic element. • A unique patterned and connected pore microstructure network exists. • Micro-channels are connected through micro-pores present at interfaces regions. • Three distinct pore volume domains exists in hardened microstructure. • Volume and frequency of pore, hydrated, and unhydrated clusters are dissimilar. Microstructural phases and mechanical properties of lamellar 3D-printed and cast hardened cement paste (hcp) elements were investigated using a lab-based X-ray microscope at two levels of magnification (0.4× and 4×). K-means clustering was used for quantitative image analysis. The entire volume of intact 3-days-old 3D-printed and cast hcp elements was characterized at 0.4× magnification. Three microstructural features (macro-pores, micro-channels, and interfacial micro-pores) were found to reside in three distinct pore size domains. The largest pores of the 3D-printed element were larger than the largest pores of the reference cast hcp element. Moreover, the smallest pore sizes of the 3D-printed element were found to be smaller than those present in the cast counterparts. Micro-channels were found to be connected to one another through the micro-pores present at interfacial regions, indicating the presence of a uniquely patterned and interconnected pore network. The role of locally weak and porous interfaces on mechanical response and fracture properties is discussed.

36 MATERIALS SCIENCE↗
DOE OSTI2022

On an Extensive Late Hydrologic Event in Gale Crater as Indicated by Water‐Rich Fracture Halos

Abstract We analyze spatially pervasive, light‐toned “halos” associated with fractures in a sedimentary unit (Stimson) of Gale crater, Mars, and report a similar network of halos discovered in a separate geologic group (Bradbury). Through a dedicated active neutron measurement campaign, we provide independent confirmation of the water‐rich nature of these features. Together with mineralogical and geochemical data, these features are consistent with abundant hydrated amorphous silica (opal‐A). We suggest that the mineral and amorphous assemblages are indicative of formation under low‐temperature and predominantly low‐pH conditions (passive silica enrichment) with minor contribution of silica (active silica enrichment) from adjacent units. We show that there is significant amorphous silica in the array of sedimentary rocks in Gale crater, allowing them to play a role in an active silica enrichment phase of halo formation. We suggest that the involved alteration event was short lived and our finding of vast halo networks in a distant, older unit implies a more vast network of hydrologic subsurface conduits than previously known. This relatively recent subsurface hydrologic system was present long after the transition from a warm and wet to a cold and dry Martian environment, extending the habitability conditions on Mars to an epoch that is generally considered not favorable for life on the surface. Finally, our bulk H quantification of these features, which ranges from ∼3–6 wt% H 2 O‐equivalent‐H, suggests that the amorphous material in halos hosts ample supplies of readily released water, making them a considerable resource at the otherwise dry Martian equator.

Gabriel, Travis S. J.↗
DOE OSTI2022

Layered supramolecular hydrogels from thioglycosides

Low molecular weight hydrogels are made of small molecules that aggregate via noncovalent interactions. Here, comprehensive characterization of the physical and chemical properties of hydrogels made from thioglycolipids of the disaccharides lactose and cellobiose with simple alkyl chains is reported. While thiolactoside hydrogels are robust, thiocellobioside gels are metastable, precipitating over time into fibrous crystals that can be entangled to create pseudo-hydrogels. Rheology confirms the viscoelastic solid nature of these hydrogels with storage moduli ranging from 10–600 kPa. Additionally, thiolactoside hydrogels are thixotropic which is a desirable property for many potential applications. Freeze-fracture electron microscopy of xerogels shows layers of stacked sheets that are entangled into networks. These structures are unique compared to the fibers or ribbons typically reported for hydrogels. Differential scanning calorimetry provides gel-to-liquid phase transition temperatures ranging from 30 to 80 °C. Prodan fluorescence spectroscopy allows assignment of phase transitions in the gels and other lyotropic phases of high concentration samples. Phase diagrams are estimated for all hydrogels at 1–10 wt% from 5 to ≥ 80 °C. In conclusion, these hydrogels represent a series of interesting materials with unique properties that make them attractive for numerous potential applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Closing the Loop between In Situ Stress Complexity and EGS Fracture Complexity

We present an agent-guided approach to CAD geometry decomposition that automates hex/hybrid meshing with graph neural networks (GNNs) to accelerate next-generation ModSim workflows. Our end-to-end pipeline (i) reduces 3D boundary-representation (B-Rep) models to a 2D chordal axis skeleton (CAT) and then to a 1D bipartite graph of surface and curve nodes, (ii) assigns per node labels as Cubit® WebCut actions, (iii) trains a multi-action GNN under supervised learning, and (iv) predicts five surface-node and three curve-node actions on out-of-distribution test geometries. Each graph node carries geometric, topological, and meshing attributes drawn from the B-Rep “skin” and CAT “skeleton,” with two-way mappings across 3D↔2D↔1D representations to maintain traceability back to 3D CAD. The supervised learning model exhibits stable convergence of the binary cross-entropy loss and achieves 98.7% accuracy on unseen lattice models. To operationalize decision-making, we rank predicted commands by geometric significance and prototyped the agent-guided workflow through the Cubit® Meshing PowerTool GUI. As a stretch goal, we explore reinforcement learning (RL) to reduce or remove label requirements and to learn policies for action sequences that maximize total reward (e.g., size of hex-meshable regions and resulting hex mesh quality). When all-hex meshing is not feasible, the agent assists in producing hybrid meshes—prioritizing hex in critical regions and transitioning to tetrahedral elements (tets) elsewhere—maintaining fidelity while ensuring robustness. The overarching objective is to replace manual, heuristics-based decomposition with data-driven, reproducible automation, cutting meshing turnaround time by orders of magnitude. We anticipate direct impact on simulation workflows through intelligent, scalable decomposition of complex CAD models into hex-meshable subdomains.

42 ENGINEERING↗