Core Model Research Articles

High-resolution and reduced-order multi-physics simulation on thermal–hydraulic and thermoelectric characteristics of the thermionic space reactor core

The simulation of a thermionic space reactor core necessitates a comprehensive consideration of the intricate interplay between fluid flow, heat transfer, and thermionic energy conversion, constituting a multi-physics coupling challenge. Given the absence of a thermionic conversion model in contemporary CFD (Computational Fluid Dynamics) codes, accurately simulating the thermal–hydraulic and thermo-electric behavior of thermionic reactors in existing CFD software is particularly challenging. To analyze the multi-physics coupling phenomena in thermionic reactors, this paper revises the conjugate heat transfer solver in the proprietary 3D (three-dimensional) finite-volume CFD code, mFVM (modular Finite-Volume Method Code), to enable bidirectional coupling with the thermionic conversion model. This enhancement aims to predict the high-resolution three-dimensional temperature field and thermoelectric characteristics of the thermionic reactor core. Furthermore, a reduced-order multi-physics model of the thermionic reactor core is established using RESYS (Reactor System Simulation Code). The numerical simulation outcomes for both the high-resolution and reduced-order multi-physics models, pertaining to electrically heated and fission-heated TFEs (Thermionic Fuel Elements), are validated against experimental data. The results indicate that the emitter electron cooling effects caused by thermionic emission reduced the maximum temperature of emitter by about 200 K for both electrically heated and fission-heated TFE. Therefore, a bidirectional coupling between the thermal–hydraulic model and the thermionic conversion model is essential for precisely calculating the temperature field within the TFE and the thermionic reactor core. Thereafter, both the high-resolution model and the reduced-order model are utilized to investigate the thermal–hydraulic and thermoelectric characteristics of the TOPAZ-II reactor core. The calculated electrical power of the TOPAZ-II reactor is 5.39 kWe using the mFVM model and 5.47 kWe using the RESYS model. Finally, an analysis of the thermoelectric conversion characteristics based on the simulation results of the reduced-order model reveals that the TOPAZ-II reactor system is capable of load-following only when the load resistance exceeds 0.06 Ω under nominal full power operation conditions.

Analytical models of supermassive black holes in galaxies surrounded by dark matter halos

In this Letter, we present five analytical models in closed forms, each representing a supermassive black hole (SMBH) located at the center of a galaxy surrounded by dark matter (DM) halo. The density profile of the halo vanishes inside twice the Schwarzschild radius of the hole and satisfies the weak, strong, and dominant energy conditions. The spacetime are asymptotically flat, and the difference among the models lies in the slopes of the density profiles in the spike and regions far from the center of the galaxy. Three of them represent cusp models, whereas the other two represent core models. With the well-known (generalized) Newman-Janis algorithm, rotating SMBHs with DM halos can be easily constructed from these models.

Competency model development: The backbone of successful stealth assessments

AbstractBackgroundStealth assessment is a learning analytics method, which leverages the collection and analysis of learners' interaction data to make real‐time inferences about their learning. Employed in digital learning environments, stealth assessment helps researchers, educators, and teachers evaluate learners' competencies and customize the learning experience to their specific needs. This adaptability is closely intertwined with theories related to learning, engagement, and motivation. The foundation of stealth assessment rests on evidence‐cantered design (ECD), consisting of four core models: the Competency Model (CM), Evidence Model, Task Model, and Assembly Model.ObjectiveThe first step in designing a stealth assessment entails producing operational definitions of the constructs to be assessed. The CM establishes a framework of latent variables representing the target constructs, as well as their interrelations. When developing the CM, assessment designers must produce clear descriptions of the claims associated with the latent variables and their states, as well as sketch out how the competencies can be measured using assessment tasks. As the designers elaborate on the assessment model, the CM definitions need to be revisited to make sure they work with the scope and constraints of the assessment. Although this is the first step, problems at this stage may result in an assessment that does not meet the intended purpose. The objective of this paper is to elucidate the necessary steps for CM development and to highlight potential challenges in the process, along with strategies for addressing them, particularly for designers without much formal assessment experience.MethodThis paper is a methodological exposition, showcasing five examples of CM development. Specifically, we conducted a qualitative retrospective analysis of the CM development procedure, wherein participants unfamiliar with ECD applied the framework and showcased their work. In a stealth assessment course, four groups of students (novice stealth assessment designers) engaged in developing stealth assessments for challenging‐to‐measure constructs across four distinct projects. During their CM development process, we observed various activities to pinpoint areas of difficulty.ResultsThis paper presents five illustrative examples, including one for assessing physics understanding and four for the development of CMs for four complex competencies: (1) systems thinking, (2) online information credibility evaluation, (3) computational thinking, and (4) collaborative creativity. Each example represents a case in CM development, offering valuable insights.ConclusionThe paper concludes by discussing several guidelines derived from the examples discussed. Emphasizing the importance of dedicating ample time to fine‐tune CMs can significantly enhance the accuracy of assessments related to learners' knowledge and skills. It underscores the significance of qualitative phases in crafting comprehensive stealth assessments, such as CMs, alongside the quantitative statistical modeling and technical aspects of these assessments.

Health technology assessment in musculoskeletal radiology: the case study of EOSedge™

ObjectivesHealth technology assessment (HTA) is a systematic process used to evaluate the properties and effects of healthcare technologies within their intended use context. This paper describes the adoption of HTA process to assess the adoption of the EOSedge™ system in clinical practice.MethodsThe EOSedge™ system is a digital radiography system that delivers whole-body, high-quality 2D/3D biplanar images covering the complete set of musculoskeletal and orthopedic exams. Full HTA model was chosen using the EUnetHTA Core Model® version 3.0. The HTA Core Model organizes the information into nine domains. Information was researched and obtained by consulting the manufacturers’ user manuals, scientific literature, and institutional sites for regulatory aspects.ResultsAll nine domains of the EUnetHTA Core Model® helped conduct the HTA of the EOSedge, including (1) description and technical characteristics of the technology; (2) health problem and current clinical practice; (3) safety; (4) clinical effectiveness; (5) organizational aspects; (6) economic evaluation; (7) impact on the patient; (8) ethical aspects; and (9) legal aspects.ConclusionsEOS technologies may be a viable alternative to conventional radiographs. EOSedge has the same intended use and similar indications for use, technological characteristics, and operation principles as the EOS System and provides significant dose reduction factors for whole spine imaging compared to the EOS System without compromising image quality. Regarding the impact of EOS imaging on patient outcomes, most studies aim to establish technical ability without evaluating their ability to improve patient outcomes; thus, more studies on this aspect are warranted.

Analysis of the USM Lecturer PPKM application portal using COBIT 2019 Framework

The USM lecturer PPKM application portal information system provides services and information regarding the research and community service activities of USM lecturers. The services available in this application include PPKM proposals, PPKM assessments, PPKM evaluations, progress reports, contracts, assignment letters and presentation schedules. To improve the system services that have been used so far, analysis and evaluation are needed so that system performance can provide accurate information and according to needs. The purpose of this research is to analyze the performance of applications using the COBIT 2019 framework. The method used is a mix method, which combines data from interviews and observations and data from questionnaires. The research subjects consisted of 7 (seven) IT admins and 30 USM permanent lecturers. The COBIT 2019 framework used as the basis for analysis consists of 11 factors, namely: Enterprise strategy; Enterprise goals; Risk Profile; IT Related Issue; IT Threat Landscape; Compliance Requirement; Role Of IT; IT Sourcing Model; IT Implementation Method; Technology Adaptation Strategy; and Enterprise Strategy. The results showed that there are 2 (two) objectives that need to go to the core model evaluation stage, namely BAI03 and BAI07. The results of the maturity analysis on BAI03 are at level 2, which is 67.1%. While the results of the maturity analysis on BAI07 show results of 72.7%.

Atomistic simulation of temperature dependence of tensile properties and estimation of DBTT of Cu–Ag core–shell nanowires

An atomistic model of Cu-Ag core–shell nanowire using molecular dynamics is proposed. The simulation model was utilized for the evolution of temperature dependent (in the range of 10–500 K) tensile behavior and the identification of ductile-to-brittle transition temperature (DBTT) of single-crystal Cu-Ag core–shell nanowire. The evolution of defects such as point defects, dislocations, and stacking faults, arising from tensile deformation in the mentioned temperature rage were analyzed. The Wigner-Seitz Defect analysis, Dislocation analysis, and Common Neighbor analysis methods were implemented in this study to estimate the various defects concentration in the nanowire. The study identified that Cu-Ag core–shell (core diameter = 4 nm and shell thickness = 1 nm nanowire) exhibits DBTT somewhere in the temperature between 25 K to 30 K. The modulus of elasticity found to be increasing from 19.7 TPa at 10 K to 23.7 TPa at 30 K (within brittle regime), followed by a steep fall to 14.9 TPa at 500 K where fracture seems to be in ductile mode. The observed mechanical properties of the nanowire are discussed in the article in view of the dislocation and stacking faults generation in the core–shell nanostructures.

GranoScan: an AI-powered mobile app for in-field identification of biotic threats of wheat.

Capitalizing on the widespread adoption of smartphones among farmers and the application of artificial intelligence in computer vision, a variety of mobile applications have recently emerged in the agricultural domain. This paper introduces GranoScan, a freely available mobile app accessible on major online platforms, specifically designed for the real-time detection and identification of over 80 threats affecting wheat in the Mediterranean region. Developed through a co-design methodology involving direct collaboration with Italian farmers, this participatory approach resulted in an app featuring: (i) a graphical interface optimized for diverse in-field lighting conditions, (ii) a user-friendly interface allowing swift selection from a predefined menu, (iii) operability even in low or no connectivity, (iv) a straightforward operational guide, and (v) the ability to specify an area of interest in the photo for targeted threat identification. Underpinning GranoScan is a deep learning architecture named efficient minimal adaptive ensembling that was used to obtain accurate and robust artificial intelligence models. The method is based on an ensembling strategy that uses as core models two instances of the EfficientNet-b0 architecture, selected through the weighted F1-score. In this phase a very good precision is reached with peaks of 100% for pests, as well as in leaf damage and root disease tasks, and in some classes of spike and stem disease tasks. For weeds in the post-germination phase, the precision values range between 80% and 100%, while 100% is reached in all the classes for pre-flowering weeds, except one. Regarding recognition accuracy towards end-users in-field photos, GranoScan achieved good performances, with a mean accuracy of 77% and 95% for leaf diseases and for spike, stem and root diseases, respectively. Pests gained an accuracy of up to 94%, while for weeds the app shows a great ability (100% accuracy) in recognizing whether the target weed is a dicot or monocot and 60% accuracy for distinguishing species in both the post-germination and pre-flowering stage. Our precision and accuracy results conform to or outperform those of other studies deploying artificial intelligence models on mobile devices, confirming that GranoScan is a valuable tool also in challenging outdoor conditions.

Precision glass molding technology for XY polynomial freeform optical elements with simulations and experiments

Precision glass molding (PGM) technology, as an effective method for mass-producing glass lenses, is relatively mature in the molding process of aspheric lenses, but the glass molding technology for freeform optical elements is still in its infancy. For freeform optical elements, processing by conventional ultra-precision methods requires multiple processes and the resulting costs are high, while processing by PGM is efficient and inexpensive. Therefore, this paper investigates the molding technology of freeform lenses, the pre-compensation model of the freeform mold core is established, and predicts the residual stresses of freeform lenses after molding by the finite element method. Three different process parameters, molding temperature, molding rate and molding force, are verified. Experimental and simulation results show that the trends of residual stresses for the three process parameters are consistent. The optimal process parameters of the molding process are determined, under which the PV value of the molding lens is around 1.5µm. The experimental results show that the PV value of the molded lens is reduced to less than 1µm after using the pre-compensated mold core, which proves the validity of the pre-compensated model.

CohortNet: Empowering Cohort Discovery for Interpretable Healthcare Analytics

Cohort studies are of significant importance in the field of healthcare analytics. However, existing methods typically involve manual, labor-intensive, and expert-driven pattern definitions or rely on simplistic clustering techniques that lack medical relevance. Automating cohort studies with interpretable patterns has great potential to facilitate healthcare analytics and data management but remains an unmet need in prior research efforts. In this paper, we present a cohort auto-discovery framework for interpretable healthcare analytics. It focuses on the effective identification, representation, and exploitation of cohorts characterized by medically meaningful patterns. In the framework, we propose CohortNet, a core model that can learn fine-grained patient representations by separately processing each feature, considering both individual feature trends and feature interactions at each time step. Subsequently, it employs K-Means in an adaptive manner to classify each feature into distinct states and a heuristic cohort exploration strategy to effectively discover substantial cohorts with concrete patterns. For each identified cohort, it learns comprehensive cohort representations with credible evidence through associated patient retrieval. Ultimately, given a new patient, CohortNet can leverage relevant cohorts with distinguished importance which can provide a more holistic understanding of the patient's conditions. Extensive experiments on three real-world datasets demonstrate that it consistently outperforms state-of-the-art approaches, resulting in improvements in AUC-PR scores ranging from 2.8% to 4.1%, and offers interpretable insights from diverse perspectives in a top-down fashion.

Tidal Seismicity in the Moon and Implications for the Rocky Interior of Europa

Seismology is a powerful tool for probing the deep interiors of planetary bodies. Just as deep moonquakes triggered by Earth’s tides occur on the Moon, as observed by the Apollo seismometers, icy moons of the giant planets may also have seismically active deep interiors, opening up future prospects for in situ seismic investigations at their surfaces. Of notable interest is Jupiter’s moon Europa, with its dynamic ice shell and potentially habitable subsurface ocean. In this work, we use different interior models of the Moon and model the tidal stress inside them to determine the most likely times and locations for the triggering of tidal moonquakes. Using the Mohr–Coulomb failure criterion, we derive cohesion and friction values for the lunar interior to match the observations of deep moonquakes by Apollo at a depth between 700 and 1200 km. By extending the same approach to different interior models of Europa, we show that Europa quakes triggered by the tides of Jupiter are 10 times more likely to occur than tidal moonquakes. The strength and depth of these tidal Europa quakes (euroquakes) strongly depend on the interior structure, with stronger events at the core–mantle boundary for liquid core models, while solid core models can be more prone to failure at the bottom of the ocean floor. Models without a metallic core favor failure in the upper third of the mantle with event strength similar to that in the solid core models.

Electrically anisotropic structure of the Rocky Mountain Trench near Valemount, British Columbia inferred from magnetotellurics: implications for geothermal exploration

Canoe Reach is a region of high geothermal potential on a segment of the Southern Rocky Mountain Trench fault (SRMTF) with highly metamorphosed and structurally complex wall rocks, near Valemount, British Columbia. This study contains analyses of magnetotelluric data collected at Canoe Reach accounting for electrical anisotropy, which is not often considered during geothermal exploration. Isotropic and anisotropic 3D inversions are used due to signs of electrical anisotropy in the Canoe Reach magnetotelluric data and the presence of visibly anisotropic geological structure. At Canoe Reach North, the anisotropic model is preferred for its simpler structure and consistency with the mapped geology. An anisotropic feature in the footwall of the steeply southwest-dipping SRMTF has a low resistivity in the fault-perpendicular direction and a high resistivity in the vertical direction, which is more easily explained by conductive minerals than by fluids in the highly metamorphosed gneiss. An exploration well in the SRMTF footwall encountered two graphite seams with thicknesses ≥1 m, supporting the interpretation of anisotropic resistivity due to conductive minerals. A strong resistivity contrast across the SRMTF suggests juxtaposition of different lithologies, challenging existing interpretations of SRMTF displacement at Canoe Reach. At Canoe Reach South, anisotropic features near the Canoe River thermal spring with a high resistivity in the fault-perpendicular direction and low resistivity in the vertical direction are consistent with fault core and damage zone models. Magnetotelluric data may be sensitive to permeability anisotropy of fault zones, and the use of electrically anisotropic inversions should be considered for these settings.

Impact load identification method based on frequency response pattern recognition and dynamic sensor filter strategy

Identification of impact loads plays important role in marine structures health monitoring but is difficult to be measured directly most time. This study investigates a two-stage framework for impact load localization and reconstruction, consisting of load region identification and local refined nodal search. For the region identification, a novel frequency response feature preprocessing method based on FFT is proposed and incorporated into a multi-layer perceptron (MLP) neural network as the embedding function of the Matching Network (MN), the core model adopted for pattern recognition. Based on the region probabilities predicted by MN, a local refined nodal search strategy is provided, which is initialized by a region correction method for amending the possible region misclassification and further guided by error metrics with iteration search strategy. Moreover, the inverse problem in this study is formulated in the discretized state space expression with the reduced modal coordinates. For improving the load inverse accuracy affected by Zero Order Hold (ZOH) simplification in this formulation, a dynamic sensor filter strategy is provided. Eventually, a numerical experiment of impact load identification on a steel plate is performed and discussed, whose results indicate the validity and robustness of the proposed method.

Low-Grade Ilmenite Leaching Kinetics Using Hydrochloric Acid: RSM and SCM Approaches

Minerals containing TiO2 are common in Indonesia, such as ilmenite in iron sand deposits scattered along the country's coasts. Ilmenite is an important source of titanium. One method for making TiO2 from ilmenite is by solubilizing both the Fe and Ti elements in HCl and then immediately hydrolyze the Ti. The leaching of low-grade ilmenite (ground to 0.177-0.149 mm) is studied kinetically by HCl in a stirred reactor. The research was conducted using the caustic fusion method followed by HCl leaching. The leaching reaction kinetics at the optimum conditions are analyzed using response surface methodology (RSM) with a second-order polynomial equation model and SSE with the shrinking core model (SCM). The results showed that HCl concentration and leaching time were directly proportional to the leached titanium concentration. In contrast, the leaching temperature was inversely proportional. The optimum operating conditions were obtained at a temperature of 30 °C, 9 M HCl, and 120 min of leaching time. The shrinking core model is a better representation of the kinetics than RSM with a second-order polynomial equation model. Based on SCM, the rate of the leaching reaction of titanium from low-grade ilmenite is controlled by diffusion through the ash layer.

Kinetics and Thermodynamics Study of Ammonia Leaching on Spent LMR-NMC Battery Cathodes

The recycling of spent lithium NMC-type batteries, widely used in electric vehicles, presents a challenge due to manganese content, which complicates metal separation and purification. This study explored a selective leaching process using ammonia to recover metals from high-manganese-content LMR-NMC cathodes. By adjusting the (NH4)2SO4 reagent concentration to 1–2 M and maintaining the temperature between 50–80 °C, the recovery rates of lithium, nickel and cobalt metals were enhanced, leaving manganese primarily as residue in the form of Mn(OH)₂ and (NH4)2Mn(SO4)2. A kinetics model, integrating an equilibrium-shrinking core model with a modified temperature-dependent Arrhenius approach, accurately simulates the metal recovery. The activation energies of the forward leaching reactions of Li, Ni, and Co were respectively (1.4331±0.0036)×105, (1.5494±0.0034)×105, and (1.5743±0.0040)×105 J/mol, while those for the backward reactions were (5.3307±0.0041)×105, (2.4753±0.0093)×105, and (1.6289±0.0092)×105 J/mol, respectively. The leaching mechanism was found to be exothermic, which allows maximum recovery at low temperatures. The findings highlight ammonia’s effectiveness as a selective leachant, significantly reducing manganese in the leaching solution, and streamlining nickel and cobalt separation, thus enhancing the recycling process’s efficiency and sustainability.

Decomposition of zinc ferrite-based solid waste by reductive leaching: Leaching kinetics and mechanism

Zinc ferrite-based solid waste is a vital zinc and iron secondary source in view of the numerous zinc ferrite will be ineluctably produced and enters into slags or residues during zinc smelting process. In this study, a reductive leaching process with zinc sulfide powder was employed to decompose zinc ferrite from solid waste. The leaching efficiency of zinc and iron can reach 90.27 % and 81.96 %, respectively, while the process went to the 90th min at 90 ℃ under the conditions: sulfuric acid concentration of 500 g/L, liquid-to-solid ratio of 20 mL g−1, mass ratio of silver flotation tailings to zinc sulfide of 5/1, silver flotation tailings particle size of + 180–250 μm, rotary speed of 400 rpm. Then, this leaching process confirmed to shrinking core model and was conducted by surface chemical reaction after kinetics and thermodynamics investigations. Ultimately, according to various tested data, the corresponding reaction mechanism was speculated as: Fe3+ was reduced to Fe2+ by zinc sulfide and hydrogen sulfide which was the product of zinc sulfide with H+, resulting in potential reduction and H+ activity elevation, accelerating zinc dissolution eventually. Meanwhile, almost all zinc sulfide and hydrogen sulfide were oxidized to monomorphic sulfur by Fe3+ where the utilization ratio of zinc sulfide was 97.28 %.

Cluster Capillary Core Model for the Calculation of the Relative Phase Permeability for Oil and Water Filtration

Cluster Capillary Core Model for the Calculation of the Relative Phase Permeability for Oil and Water Filtration

Innovative multifunctional nanocapsules with antibacterial, perfume, and UV-initiated coating properties

Polymer nanocapsules containing fragrance were fabricated for antibacterial, pleasing scent and UV-coating applications. Poly (2-methacryloyloxy dodecyl dimethyl ammonium chloride-4-allyloxy-2-hydroxybenzophenone)-block-polymethyl methacrylate-iodide (P(QAC12-BP)-b-PMMA-I) was first synthesized by solution iodine transfer polymerization (solution ITP) for use as a polymerizable surfactant in miniemulsion polymerization where benzalkonium chloride (BKC) was used as a cosurfactant. The nanocapsules were prepared using poly(methyl methacrylate–divinylbenzene) (P(MMA–DVB)) as a shell, whereas mangosteen oil (MO) was used as a fragrance core model for aroma. High colloidal stability nanocapsules with a size of about 213 nm were obtained. The nanocapsule surface contained QAC12 segments of 15.10 µmol/g-capsule (UV measurement), giving the high positive charge (+70 mV). An essential oil encapsulated inside the nanocapsule had a high encapsulation efficiency (>90%). The nanocapsules effectively inhibited bacterial growth because of QAC12 and BKC on their surface. The nanocapsules are able to coat the substrate via UV-activated covalent bonds of the BP segment.

A multi-grain multi-layer astrochemical model with variable desorption energy for surface species

Interstellar surface chemistry is a complex process that occurs in icy layers that have accumulated onto grains of different sizes. The efficiency of the surface processes often depends on the immediate environment of the adsorbed molecules. We investigated how gas-grain chemistry changes when the surface molecule binding energy is modified, depending on the properties of the surface. In a gas-grain astrochemical model, molecular binding energy gradually changes for three different environments -- (1) the bare grain surface, (2) polar water-dominated ices, and (3) weakly polar carbon monoxide-dominated ices. In addition to diffusion, evaporation, and chemical desorption, photodesorption was also made binding energy-dependent, in line with experimental results. These phenomena occur in a collapsing prestellar core model that considers five grain sizes with ices arranged into four layers. Variable desorption energy moderately affects gas-grain chemistry. Bare-grain effects slow down ice accumulation, while easier diffusion of molecules on weakly polar ices promotes the production of carbon dioxide. Efficient chemical desorption from bare grains significantly delays the appearance of the first ice monolayer. The combination of multiple aspects of grain surface chemistry creates a gas-ice balance that is different from simpler models. The composition of the interstellar ices is regulated by several binding-energy dependent desorption mechanisms. Their actions overlap in time and space, explaining the similar proportions of major ice components (water and carbon oxides) observed in all directions.

MCR-Tree: An Efficient Index for Multi-dimensional Core Search

Core models are well-known cohesive subgraph models for graph analytics that have been extensively studied. These models, including (α, β)-core, (k, l)-core, and k -core, have multiple parameters, which are referred to as multi-dimensional cores. The goal of core search is to retrieve subgraphs from a graph that satisfy the semantics of a given core model. In the literature, various indexes have been proposed to accelerate core search for different core models. However, existing indexes suffer from several limitations, such as significant redundancy, lack of scalability with respect to the number of parameters, limited generality, and inadequate consideration of index maintenance. To address these limitations, in this paper, we thoroughly investigate the problem of multi-dimensional core search. In particular, we propose a novel index called MCR-Tree, which can be applied to different core models. The MCR-Tree projects all vertices into a multi-dimensional space by leveraging the skyline corenesses, which are indexed by an R-tree. Furthermore, the MCR-Tree integrates the connectivity information of subgraphs into the nodes of the R-tree to facilitate multi-dimensional core search. Subsequently, an efficient branch-and-bound algorithm is designed to perform multi-dimensional core search by traversing the MCR-Tree. Additionally, we discuss how to maintain the MCR-Tree for graph updates. Extensive experiments demonstrate that the MCR-Tree is up to two orders of magnitude smaller than existing indexes and the MCR-Tree-based core search method is up to an order of magnitude faster than existing algorithms.

State of the Art on Relative Permeability Hysteresis in Porous Media: Petroleum Engineering Application

This paper delivers an examination of relative permeability hysteresis in porous media in the field of petroleum engineering, encompassing mathematical modeling, experimental studies, and their practical implications. It explores two-phase and three-phase models, elucidating the generation of scanning curves and their applications in various porous materials. Building on the research of traditional relative permeability hysteresis models, we have incorporated literature on forward calculations of relative permeability based on digital rock core models. This offers a new perspective for studying the hysteresis effect in relative permeability. Additionally, it compiles insights from direct relative permeability and flow-through experiments, accentuating the methodologies and key findings. With a focus on enhanced oil recovery (EOR), carbon capture, utilization and sequestration (CCUS), and hydrogen storage applications, the paper identifies existing research voids and proposes avenues for future inquiry, laying the groundwork for advancing recovery techniques in oil and gas sectors.