Representation Of Microstructure Research Articles

Raman activities of nitrogen reduction and ammonia oxidation intermediates on the high-entropy alloy CoCuFeMoNi catalytic surface.

We developed a computational framework to extract the Raman spectra of nitrogen reduction and ammonia oxidation intermediates on high-entropy alloy (HEA) surfaces, integrating density functional theory with microstructural representations to account for the inherent lattice randomness in these materials. As a case study, we computed the Raman activities of intermediates (N2*, NNH*, N*, NH*, and NH3*) and H* adsorption on CoCuFeMoNi HEA surfaces. A comprehensive map of Raman peaks was generated and assigned to specific vibrational modes. The method highlighted the effects of lattice randomness on the Raman spectra compared to those of adsorbates on single-element catalysts. For instance, our results showed that the adsorbed N2 exhibits Raman modes that are dependent on whether the adsorption is vertical or horizontal. These peak differences could serve as unique fingerprints to identify nitrogen reduction reaction pathways. Moreover, it is also possible to detect surface poisoning by hydrogen, a common issue in reductive environments, due to the high-frequency peaks of H* compared to the typical N-metal stretching and bending frequencies. These results provide valuable references for identifying intermediates in nitrogen reduction and ammonia oxidation reactions, offering insights into reaction mechanisms and potential surface poisoning. This approach is generalizable to other reactions and surfaces in catalysis, provided that the relevant intermediates can be identified.

The Concept of Using 3D Printing Technology in Ceramic Foundry Filter Manufacturing

AbstractThe article presents the concept of using 3D printing technology in ceramic foundry filter manufacturing. They are a crucial component for obtaining an acceptable quality of nickel superalloys by carrying out the process using the precision casting method. Commonly used filters of this type have a number of disadvantages. They are characterized by irregularity of the filtering structure, high brittleness, lack of resistance to mechanical shocks, and impacts of a stream of liquid metal. All these factors create a risk of introducing the material from the damaged filter into the casting mold, which translates into contamination of the casting alloy, and thus the occurrence of casting defects such as nonmetallic inclusions. The hope for a change in this state of affairs is the use of additive manufacturing technology in their production, which by assumption will allow to obtain a product with a repeatable shape, high mechanical resistance, and a specially designed structure regulating the flow of liquid metal into the mold during casting. The suitability of robocasting and binder jetting technology for producing filtration structures was initially assessed. The results have presented the selection of ceramic powder, as well as the development of the composition of the ceramic paste. The parameters of paste preparation and 3D printing individual processes were described. Moreover, the representative microstructures and basic mechanical properties of samples obtained by both of the compared technologies were presented. Furthermore, the final effect of prototype casting filters with a repeatable shape, manufactured with the use of both technologies, was presented, which were transferred for further technological research in a foundry producing critical aircraft engine parts. The possibilities of using each technology in various applications were also discussed.

Automated model discovery for textile structures: The unique mechanical signature of warp knitted fabrics

Textile fabrics have unique mechanical properties, which make them ideal candidates for many engineering and medical applications: They are initially flexible, nonlinearly stiffening, and ultra-anisotropic. Various studies have characterized the response of textile structures to mechanical loading; yet, our understanding of their exceptional properties and functions remains incomplete. Here we integrate biaxial testing and constitutive neural networks to automatically discover the best model and parameters to characterize warp knitted polypropylene fabrics. We use experiments from different mounting orientations, and discover interpretable anisotropic models that perform well during both training and testing. Our study shows that constitutive models for warp knitted fabrics are highly sensitive to an accurate representation of the textile microstructure, and that models with three microstructural directions outperform classical orthotropic models with only two in-plane directions. Strikingly, out of 214=16,384 possible combinations of terms, we consistently discover models with two exponential linear fourth invariant terms that inherently capture the initial flexibility of the virgin mesh and the pronounced nonlinear stiffening as the loops of the mesh tighten. We anticipate that the tools we have developed and prototyped here will generalize naturally to other textile fabrics–woven or knitted, weft knit or warp knit, polymeric or metallic–and, ultimately, will enable the robust discovery of anisotropic constitutive models for a wide variety of textile structures. Beyond discovering constitutive models, we envision to exploit automated model discovery as a novel strategy for the generative material design of wearable devices, stretchable electronics, and smart fabrics, as programmable textile metamaterials with tunable properties and functions. Our source code, data, and examples are available at https://github.com/LivingMatterLab/CANN. Statement of significanceTextile structures are rapidly gaining popularity in many biomedical applications including tissue engineering, wound healing, and surgical repair. A precise understanding of their unique mechanical properties is critical to tailor them to their specific functions. Here we integrate mechanical testing and machine learning to automatically discover the best models for knitted polypropylene fabrics. We show that warp knitted fabrics possess a complex symmetry with three distinct microstructural directions. Along these, the behavior is dominated by an exponential linear term that characterize the initial flexibility of the virgin mesh and the nonlinear stiffening as the loops of the fabric tighten. We expect that our technology will generalize naturally to other fabrics and enable the robust discovery of complex anisotropic models for a wide variety of textile structures.

Anisotropic power diagrams for polycrystal modelling: Efficient generation of curved grains via optimal transport

The microstructure of metals and foams can be effectively modelled with anisotropic power diagrams (APDs), which provide control over the shape of individual grains. One major obstacle to the wider adoption of APDs is the computational cost that is associated with their generation. We propose a novel approach to generate APDs with prescribed statistical properties, including fine control over the size of individual grains. To this end, we rely on fast optimal transport algorithms that stream well on Graphics Processing Units (GPU) and handle non-uniform, anisotropic distance functions. This allows us to find large APDs that best fit experimental data and generate synthetic high-resolution microstructures in (tens of) seconds. This unlocks their use for computational homogenisation, which is especially relevant to machine learning methods that require the generation of large collections of representative microstructures as training data. The paper is accompanied by a Python library, PyAPD, which is freely available at: www.github.com/mbuze/PyAPD.

Pizza3: A general simulation framework to simulate food-mechanical and food-deconstruction problems

Current mesh-based simulation approaches face significant challenges in continuously modeling the mechanical behaviors of foods through processing, storage, deconstruction, and digestion. This is primarily due to the limitations of continuum mechanics in dealing with systems characterized by free boundaries, substantial deformations, mechanical failures, and non–homogenized mechanical properties. The dynamic nature of food microstructure and the transformation of the food bolus, in relation to its composition, present formidable obstacles in computer-aided food design. In response, the Pizza3 project adopts an innovative methodology, utilizing an explicit microstructural representation to construct and subsequently deconstruct food products in a modular, Lego-like fashion. Central to this simulation approach are “food atoms”, conceptualized from the principles of smoothed particle hydrodynamics. These units are significantly larger than actual atoms but are finely scaled to represent both solid and liquid states of food faithfully. In solid phases, food atoms interact via pairwise forces akin to bond-peridynamic methods, thus extending the capabilities of continuum mechanics to encompass large deformations and fracturing phenomena. For liquids, the model employs artificial conservative and dissipative forces, enabling the simulation of a variety of phenomena within the framework of partial compressibility. The interaction dynamics between rigid and soft objects and fluids are accurately captured through Hertzian contact mechanics, offering a versatile parameterization applicable to impermeable (but possibly penetrable) surfaces and enforcing no-slip conditions. The efficacy of this framework is showcased through the successful modeling of three time-dependent 3D scenarios, each rigorously validated against established analytical and experimental models. Advancing beyond these initial applications, the framework is further extended to more intricate cases inadequately addressed in current literature. This extension sheds light on the underlying mechanisms of in-mouth texture perception, offering new insights and tools for food engineering and design.

Buckling behaviors prediction of biological staggered composites with finite element analysis and machine learning coupled method

Staggered structure, where mineral crystals are arranged in a staggered manner in protein matrix, is the most representative microstructure in biological composites. Because of the elongated geometry and large aspect ratio of mineral crystals, their failure mechanism under compressive loading is predominantly controlled by buckling. The microstructural complexity, diversity of the constituent materials, and the complexity of buckling behaviors present substantial challenges for the precise forecasting of the buckling behavior of biological materials. In this paper, a novel method combining finite element analysis with machine learning is proposed. Specifically, based on the large dataset obtained using the finite element method, the buckling strength of biological staggered composites was rapidly predicted using a machine learning algorithm. Herein, the effects of microstructure and component materials on buckling behavior have been investigated systematically. The results indicate that the neural network models are highly accurate in predicting critical buckling strength. Aspect ratio and modulus ratio significantly affect the critical buckling strength of staggered structures according to the results of feature importance analysis. Additionally, the post-buckling modes of staggered structures exhibit heightened sensitivity to initial geometric defects, particularly in higher-order buckling modes. Our research provides valuable insights into fast and accurate prediction of the buckling behavior of biological composites and their design.

Contrastive learning based on hierarchical graph of microstructures through directed energy deposition process to establish process-structure–property relationship via autoencoder

n this study, an innovative algorithm is developed to transform microstructures to hierarchical graphs without manual feature engineering. The hierarchical graph comprises two layers. Initially, pixel data, which includes Euler angles, phase, and position, forms the pixel-wise graphs within individual grains. Following this foundation, the grains serve as nodes, constituting the second layer. Importantly, the hierarchical graph preserves essential measurement data and structural details for training machine learning models. After that, a contrastive learning model based on hierarchical graph is designed to capture representations of microstructures obtained by the electron backscatter diffraction (EBSD) technique. This model can be directly extended for other microscopy techniques, such as Scanning Electron Microscopy (SEM), Energy-Dispersive X-ray spectroscopy (EDX), and Transmission Electron Microscopy (TEM). Using the learned microstructure representations, an autoencoder model for the directed energy deposition (DED) is developed to establish the relationship among process parameters, microstructures, and material properties, completing the process-structure–property cycle quantitatively. The performance of the present model is naturally benchmarked against two other models: a contrastive learning model based on pixel-wise graph (without manual feature engineering) and a contrastive learning model based on grain-wise graph (employing manual feature engineering). The results of the present model highlight the potential in decoding the process-structure–property relationships in the DED process.

Identifying the in-situ origin of supercluster-induced ductile precipitates in Zr-based bulk metallic glasses

A sandwich architecture including double crystal substrates and a single glass-forming droplet has been successfully designed based on the simplified liquid-bridge methods in multicomponent alloys. The multiscale precipitates mainly consisting of β-phase dendrites with a body-centered cubic (BCC) structure, cubic CuZr2 phase, and icosahedral quasicrystalline phase (I-phase) could be simultaneously identified in dissimilar regions of the glass-forming matrix during heat-induced dissolution or diffusion from crystal substrate to glass-forming droplet. The gradient distributions of crystal-like clusters not only accelerate diffusion velocity due to the collective atomic motion but also can simultaneously modulate the atomic-scale structure, mechanical response, and chemical composition via tracing a high-throughput strategy. The present work provides new insights to discover the formation mechanism of the representative microstructures strongly associated with the atomic-scale structure-property-composition relationships and is also helpful to shed light on the in-situ origin or structural mechanism of supercluster-induced multiscale precipitates in developing bulk metallic glasses (BMGs) and in-situ formed BMG composites (BMGCs).

High-fidelity level-set modeling of diffusive solid-state phase transformations for polycrystalline materials

The formation of microstructures in metallic alloys during hot metal forming involves simultaneous metallurgical complex phenomena. Traditional high-fidelity numerical frameworks used on the polycrystalline scale tend to focus on single-phase microstructures or isolate phase transformations from grain boundary migration mechanisms. The level-set method is highlighted as effective in proposing a global framework for modeling multiphase polycrystalline materials and diffusive solid-state phase transformations. This framework includes novel techniques for efficient large-scale microstructural representation, strong coupling with ThermoCalc software for real-time thermodynamic data, application for ternary alloys and beyond by taking solute drag aspects, and the use of advanced nucleation models. Numerous applications are then illustrated.

Stress localization investigation of additively manufactured GRCop-42 thin-wall structure

A full-field crystal plasticity (CP) framework is presented for the GRCop-42 alloy to study microscopic mechanical behavior and local stress heterogeneities. The microstructures of additively manufactured (AM) materials are often unique relative to conventionally processed materials, and the local thermal histories drive these differences during the build process. These thermal histories depend on the process parameters (laser power, scan speed, and scan strategy) and the part geometry. Prior research has shown that the mechanical properties of thin-walled structures can vary significantly with wall thickness due to changes in the thermal boundary conditions during manufacturing. It is, therefore, desirable to perform CP simulations based on the phenomenological constitutive model to predict the local mechanical responses induced by microstructural heterogeneities. This work generates representative microstructures based on experimentally collected grain information (i.e., texture) for grain scale stress analysis, and the material constitutive parameters are calibrated using the experimental mechanical testing data. We specifically investigated the effect of crystallographic texture and grain morphologies on the size-dependent mechanical properties of AM GRCop-42. The selection of appropriate material properties for implementing an effective free surface boundary condition and the influence of adjacent buffer layers are also discussed. Analysis of local field results reveals a strong correlation between stress localization and the initial grain orientation. However, no significant relationship between the misorientation of the individual adjacent grains and the average misorientation is observed.

Graph-enhanced deep material network: multiscale materials modeling with microstructural informatics

AbstractThis study addresses the fundamental challenge of extending the deep material network (DMN) to accommodate multiple microstructures. DMN has gained significant attention due to its ability to be used for fast and accurate nonlinear multiscale modeling while being only trained on linear elastic data. Due to its limitation to a single microstructure, various works sought to generalize it based on the macroscopic description of microstructures. In this work, we utilize a mechanistic machine learning approach grounded instead in microstructural informatics, which can potentially be used for any family of microstructures. This is achieved by learning from the graph representation of microstructures through graph neural networks. Such an approach is a first in works related to DMN. We propose a mixed graph neural network (GNN)-DMN model that can single-handedly treat multiple microstructures and derive their DMN representations. Two examples are designed to demonstrate the validity and reliability of the approach, even when it comes to the prediction of nonlinear responses for microstructures unseen during training. Furthermore, the model trained on microstructures with complex topology accurately makes inferences on microstructures created under different and simpler assumptions. Our work opens the door for the possibility of unifying the multiscale modeling of many families of microstructures under a single model, as well as new possibilities in material design.

How are microstructural defect interactions linked to simultaneous intergranular and transgranular fracture modes in polycrystalline systems?

The major objective of this investigation is to fundamentally understand and predict how intergranular (IG) and transgranular (TG) fracture modes nucleate and propagate in f.c.c. polycrystalline systems due to defects, such as total and partial dislocation densities and grain boundary (GB) structures and misorientations. A dislocation density crystalline plasticity (DCP) formulation based on the evolution and interaction of total and partial dislocation densities was integrated with a recently developed fracture approach to investigate the fracture nucleation and propagation of simultaneous multiple fracture events, including both IG and TG fracture events. The aggregate grains and GB orientations and morphologies are based on EBSD measurements that are representative of polycrystalline copper aggregates with a broad range of random high angle and low angle GBs. The predictions indicate that dislocation density pileups induce IG fracture due to interrelated stress, slip, and total and partial dislocation density accumulations and interactions for both high angle GBs (HAGBs) and low angle GBs (LAGBs). TG fracture nucleation and propagation occurred due to normal stress accumulations, which exceeds the fracture stress, along cleavage planes. Furthermore, it is shown how IG cracks transition from the GB plane to the cleavage planes as cracks nucleate and propagate. Accumulated plastic zones due to different slip system activities can impede and blunt crack propagation and fronts. These plastic zones form due to high Lomer and Shockley partial dislocation densities. These predictions, which are consistent with experimental observations, provide a fundamental understanding of how simultaneous failure modes initiate and propagate for physically representative microstructures.

Microstructure generation and full-field multi-scale analyses for short fiber reinforced thermoplastics: Application to PA66GF composites

Short Fiber Reinforced Thermoplastics (SFRTs) like PA66GF composites are highly heterogeneous materials with a rather complex microstructure such that the characterization and the prediction of their mechanical behavior remain quite challenging. So far, most of the research efforts have employed phenomenological and mean-field multi-scale models to deal with SFRTs. The present contribution rather focuses on a full-field multi-scale approach that incorporates advanced techniques in terms of microstructural representation and material modeling, allowing a deep insight of the dominating deformation mechanisms occurring in PA66GF composites. The proposed approach is based on an automatic periodic mesh generation algorithm for matrix-inclusion Representative Volume Elements (RVE) with randomly positioned fibers that follow a given Orientation Distribution Function (ODF). At the microscopic scale, while the fibers are assumed to be elastic, the behavior of the thermoplastic matrix is described by a phenomenological multi-mechanism constitutive model accounting for viscoelasticity, viscoplasticity and ductile damage. It results an advanced multi-scale model that enables to visualize the local deformation and degradation mechanisms occurring at the microscopic scale while simultaneously analyzing their influence on the macroscopic response of the composite upon monotonic, persistent and cyclic loading. The potential of the proposed approach is further evaluated by comparing the predicted responses against experimental data.

Direct FE[formula omitted] failure prediction of fused-filament fabricated parts

The rapid development of additive manufacturing technology and the consequential microstructural variations have brought the simulation aspects into focus. To capture an accurate mechanical response, multiscale methods are used to determine the macroscopic constitutive behaviour. Among these methods, the recently introduced Direct FE2 (DFE2) technique has shown some success in predicting material behaviour via the direct incorporation of representative microstructure volumes into the macroscopic structure. The present study highlights the predictive modelling capabilities of DFE2 linked with an element elimination technique (EET) in investigating the performance of fused-filament fabrication (FFF) samples under tension and compression. Parts were manufactured by FFF with various raster angles to vary the failure characteristics. Their mechanical responses and fracture behaviours were simulated by DFE2 and benchmarked against conventional finite element (FE) analysis, highlighting that the results of DFE2 were competitive against FE. Most importantly, the combination of EET with DFE2 resulted in predictive insights regarding changes in the failure surface for samples with varying raster angles.

Stress Relaxation Cracking in 347H Stainless Steel Arc Welds: Susceptibility Evaluation of Heat-Affected Zone

Stress relaxation cracking (SRC) is considered one of the major failure mechanisms for 347H stainless steel welds at elevated service temperatures or during post weld heat treatment (PWHT), especially within the heat-affected zone (HAZ). This work focuses on the characterization of SRC susceptibility within 347H physically simulated arc welded HAZ at elevated temperatures. A four-step SRC thermomechanical test in combination with finite element modeling (FEM) of the welding and testing processes is developed to establish a susceptibility map for HAZ. The test first runs a thermal cycle with three different peak temperatures (1335, 1275, and 1150 °C) to duplicate representative HAZ subzone microstructures, followed by time-to-failure examination under a variety of pre-stress (260–600 MPa) and pre-strain conditions (0.03–0.19) as a function of reheat temperatures between 750 and 1050 °C. With the aid of FEM, SRC susceptibility maps are generated to identify the threshold stress, plastic strain, and creep strain as a function of test temperature. It was found out that HAZ subzone with a lower peak temperature (1150 °C) appears to be slightly less susceptible to SRC than the other two subzones that experienced higher peak temperatures. Generally, time-to-fracture reduces with increasing initially applied stress and strain for all test temperatures. The pre-stress thresholds decrease from about 500 to 330 MPa as the testing temperature increases from 800 to 1050 °C, while the corresponding initial plastic strain thresholds reduces from 0.15 to 0.06. The SRC susceptibility was also evaluated through the Larson–Miller Parameter (LMP) analysis as a function of plastic strain, initial stress and starting stress upon reaching the testing temperature, respectively. The 1050 °C test with a high pre-applied strain (0.1) exhibits an extremely short time to failure (t = 3 s) that lies outside the general trend in LMP analysis. Additionally, it was identified that a plastic strain above 0.07 is identified to significantly reduce the bulk creep strain tolerance to fracture and therefore increases SRC susceptibility. Hardness measurement and fractography analysis indicated that the strain aging of niobium carbonitrides and other potential phases in conjunction with intergranular precipitates contributes to an increase in microhardness and increased intergranular cracking susceptibility.

Micro-mechanics investigation of heterogeneous deformation fields and crack initiation driven by the local stored energy density in austenitic stainless steel welded joints

This work investigates the heterogeneous deformation and failure of HR3C austenitic stainless steel welded joints at room and typical service temperatures. Such types of welded joints are widely used in the new generation of fossil fuel power stations and are known to suffer from premature high temperature failure. Observation of in-service failures revealed that cracks may nucleate either in the heat affected zone or in the weld metal. The main objective of this work is to identify the local microstructural conditions and stress–strain fields responsible for both ductile failure and micro-crack nucleation within the weldment at low and high temperatures, respectively. To that purpose, a novel multi-scale micromechanics-based modelling framework is proposed. It relies on representative weld microstructure models digitally reconstructed from electron backscatter diffraction measurements, and dislocation density-based crystal plasticity models to describe the behaviour of individual grains in each weld region. A novel thermo-dynamically consistent relation for the configuration stored energy per unit volume is derived from the crystal plasticity framework.The single crystal models are calibrated and validated from a combination of uniaxial tensile tests on cross-weld specimens using high resolution digital image correlation techniques, representative volume elements of the polycrystals at the macro-scale, as well as micropillar compression tests at the scale of the individual grains. Predictions of heterogeneous inelastic deformation fields within the weldment at both 22 °C and 665 °C, and of micropillar compression behaviour of the individual weld single crystal phases are consistent with experimental data. The experimentally observed ductile failure of the weld metal at room temperature is successfully predicted using a Gurson-type void nucleation, growth and coalescence constitutive model. The suitability of the stored strain energy density and the accumulated plastic strain as crack initiation/creep failure indicators is investigated. It was found that predicted creep lifetimes with either indicator were very similar, and that they were relatively accurate for low stress levels.

Three-Dimensional Columnar Microstructure Representation Using 2D Electron Backscatter Diffraction Data for Additive-Manufactured Haynes®282®.

This study provides a methodology for exploring the microstructural and mechanical properties of the Haynes®282® alloy produced via the Powder Bed Fusion-Electron Beam (PBF-EB) process. Employing 2D Electron Backscatter Diffraction (EBSD) data, we have successfully generated 3D representations of columnar microstructures using the Representative Volume Element (RVE) method. This methodology allowed for the validation of elastic properties through Crystal Elasticity Finite Element (CEFE) computational homogenization, revealing critical insights into the material behavior. This study highlights the importance of accurately representing the grain morphology and crystallographic texture of the material. Our findings demonstrate that created virtual models can predict directional elastic properties with a high level of accuracy, showing a maximum error of only ~5% compared to the experimental results. This precision underscores the potential of our approach for predictive modeling in Additive Manufacturing (AM), specifically for materials with complex, non-homogeneous microstructures. It can be concluded that the results uncover the intricate link between microstructural features and mechanical properties, underscoring both the challenges encountered and the critical need for the accurate representation of grain data, as well as the significance of achieving a balance in EBSD area selection, including the presence of anomalies in strongly textured microstructures.

Analytical Model of Quantitative Texture Prediction Considering Heat Transfer Based on Single-Phase Material in Laser Powder Bed Fusion

Laser powder bed fusion (LPBF) is widely used in metal additive manufacturing to create geometrically complex parts, where heat transfer and its affected temperature distribution significantly influence the parts’ materials’ microstructure and the resulting materials’ properties. Among all the microstructure representations, crystallographic orientations play a paramount role in determining the mechanical properties of materials. This paper first developed a physics-based analytical model to predict the 3D temperature distribution in PBF considering heat transfer boundary conditions; heat input using point-moving heat source solutions; and heat loss due to heat conduction, convection, and radiation. The superposition principle obtained temperature distributions based on linear heat sources and linear heat loss solutions. Then, the temperature distribution was used to analytically obtain the texture grown on a substrate with random grain orientations considering columnar-to-equiaxed transition (CET). Thus, the link between process parameters and texture was established through CET models and physical rules. Ti-6Al-4V was selected to demonstrate the capability of the analytical model in a single-phase situation. By applying advanced thermal models, the accuracy of the texture prediction was evaluated based on a comparison of experimental data from the literature and past analytical model results. Hence, this work not only provides a method of the fast analytical simulation of texture prediction in the single-phase mode for metallic materials but also paves the road for subsequent studies on microstructure-affected or texture-affected materials’ properties for both academic research and industrial applications. The prediction of single-phase material texture has never been achieved before, and the scalability has been expanded.

Effect of microvariability on electrical rock properties

SUMMARY In petrophysics, physical rock properties are typically established through laboratory measurements of individual samples. These measurements predominantly relate to the specific sample and can be challenging to associate with the rock as a whole since the physical attributes are heavily reliant on the microstructure, which can vary significantly in different areas. Thus, the obtained values have limited applicability to the entirety of the original rock mass. To examine the dependence of petrophysical measurements based on the variable microstructure, we generate sets of random 2-D microstructure representations for a sample, taking into account macroscopic parameters such as porosity and mean grain size. For each microstructure produced, we assess the electrical conductivity and evaluate how it is dependent on the microstructure’s variability. The developed workflow including microstructure modelling, finite-element simulation of electrical conductivity as well as statistical and petrophysical evaluation of the results is presented. We show that the methodology can adequately mimic the physical behaviour of real rocks, showing consistent emulation of the dependence of electrical conductivity on connected porosity according to Archie’s law across different types of pore space (microfracture, intergranular and vuggy, oomoldic pore space). Furthermore, properties such as the internal surface area and its fractal dimension as well as the electrical tortuosity are accessible for the random microstructures and show reasonable behaviour. Finally, the possibilities, challenges and meshing strategies for extending the methodology to 3-D microstructures are discussed.

Multi-Fidelity Design of Porous Microstructures for Thermofluidic Applications

Abstract As modern electronic devices are increasingly miniaturized and integrated, their performance relies more heavily on effective thermal management. In this regard, two-phase cooling methods which capitalize on thin-film evaporation atop structured porous surfaces are emerging as potential solutions. In such porous structures, the optimum heat dissipation capacity relies on two competing objectives that depend on mass and heat transfer. Optimizing these objectives for effective thermal management is challenging due to the simulation costs and the high dimensionality of the design space which is often a voxelated microstructure representation that must also be manufacturable. We address these challenges by developing a data-driven framework for designing optimal porous microstructures for cooling applications. In our framework, we leverage spectral density functions to encode the design space via a handful of interpretable variables and, in turn, efficiently search it. We develop physics-based formulas to simulate the thermofluidic properties and assess the feasibility of candidate designs based on offline image-based analyses. To decrease the reliance on expensive simulations, we generate multi-fidelity data and build emulators to find Pareto-optimal designs. We apply our approach to a canonical problem on evaporator wick design and obtain fin-like topologies in the optimal microstructures which are also characteristics often observed in industrial applications.