Search Results (71)

The mesoscopic-scale discrete element method (DEM) modeling approach demonstrated high compatibility with macroporous recycled concrete (MRC). However, existing DEM models failed to adequately balance modeling accuracy and computational efficiency for recycled aggregate (RA), replicate the three distinct interfacial transition zone (ITZ) types and pore structure of MRC, or establish a systematic calibration methodology. In this study, PFC 3D was employed to establish a randomly polyhedral RA composite model and an MRC model. A systematic methodology for parameter testing and calibration was proposed, and compressive test simulations were conducted on the MRC model. The model incorporated all components of MRC, including three types of ITZs, achieving an aggregate volume fraction of 57.7%. Errors in simulating compressive strength and elastic modulus were 3.8% and 18.2%, respectively. Compared to conventional concrete, MRC exhibits larger strain and a steeper post-peak descending portion in stress–strain curves. At peak stress, stress is concentrated in the central region and the surrounding arc-shaped zones. After peak stress, significant localized residual stress persists within specimens; both toughness and toughness retention capacity increase with rising porosity and declining compressive strength. Failure of MRC is dominated by tension rather than shear, with critical bonds determining strength accounting for only 1.4% of the total. The influence ranking of components on compressive strength is as follows: ITZ (new paste–old paste) > ITZ (new paste–natural aggregates) > new paste > old paste > ITZ (old paste–natural aggregates). The Poisson’s ratio of MRC (0.12–0.17) demonstrates a negative correlation with porosity. Predictive formulas for peak strain and elastic modulus of MRC were established, with errors of 2.6% and 3.9%, respectively. Full article

This work presents a multiscale, microstructure-aware framework for predicting fatigue strength distributions in additively manufactured (AM) alloys—specifically, laser powder bed fusion (L-PBF) AlSi10Mg and Ti-6Al-4V—by integrating density functional theory (DFT), instrumented indentation, and Bayesian inference. The methodology leverages principles common to all 3D printing (additive manufacturing) processes: layer-wise material deposition, process-induced defect formation (such as porosity and residual stress), and microstructural tailoring through parameter control, which collectively differentiate AM from conventional manufacturing. By linking DFT-derived cohesive energies with indentation-based modulus measurements and a MAP-based statistical model, we quantify the effect of additive-manufactured microstructural heterogeneity on fatigue performance. Quantitative validation demonstrates that the predicted fatigue strength distributions agree with experimental high-cycle and very-high-cycle fatigue (HCF/VHCF) data, with posterior modes and 95 % credible intervals of ${\widehat{\sigma }}_f^{\mathrm{AlSi}10\mathrm{Mg}}={86}_{-7}^{+8}\mathrm{MPa}$ and ${\widehat{\sigma }}_f^{\mathrm{Ti}–6\mathrm{Al}–4\mathrm{V}}={115}_{-9}^{+10}\mathrm{MPa}$, respectively. The resulting Woehler (S–N) curves and Paris crack-growth parameters envelop more than 92 % of the measured coupon data, confirming both accuracy and robustness. Furthermore, global sensitivity analysis reveals that volumetric porosity and residual stress account for over 70 % of the fatigue strength variance, highlighting the central role of process–structure relationships unique to AM. The presented framework thus provides a predictive, physically interpretable, and data-efficient pathway for microstructure-informed fatigue design in additively manufactured metals, and is readily extensible to other AM alloys and process variants. Full article

The soil water characteristic curve (SWCC) is a key foundation in unsaturated soil mechanics describing the relationship between matric suction and water content, which is crucial for studies on effective stress, permeability coefficients, and other soil properties. In natural environments, colluvial and residual soils typically exhibit high pore heterogeneity, and previous studies have shown that the SWCC is closely related to the distribution of pore sizes. The SWCC of soils may display either a unimodal or bimodal distribution, leading to different hydraulic behaviors. Past unsaturated slope stability analyses have used the unimodal SWCC model, but this assumption may result in evaluation errors, affecting the accuracy of seepage and slope stability analyses. This study proposes a novel bimodal hydro-mechanical coupling model to investigate the influence of bimodal SWCC representations on rainfall-induced seepage behavior and stability of unsaturated slopes. By fitting the unimodal and bimodal SWCCs with experimental data, the results show that the bimodal model provides a higher degree of fit and smaller errors, offering a more accurate description of the relationship between matric suction and effective saturation, thus improving the accuracy of soil hydraulic property assessment. Furthermore, the study established a hypothetical slope model and used field data of landslides to simulate the collapse of Babaoliao in Chiayi County, Taiwan. The results show that the bimodal model predicts slope instability 1 to 3 h earlier than the unimodal model, with the rate of change in the safety factor being about 16.6% to 25.1% higher. The research results indicate the superiority of the bimodal model in soils with dual-porosity structures. The bimodal model can improve the accuracy and reliability of slope stability assessments. Full article

In this study, we propose a self-adaptive weighted multi-mineral inversion model (SQP_AW) based on Sequential Quadratic Programming (SQP) and the Adam optimization algorithm for the accurate evaluation of mineral content in carbonate reservoir rocks, addressing the high costs of traditional experimental methods and the strong parameter dependence in geophysical inversion. The model integrates porosity curves (compensated density, compensated neutron, and acoustic time difference), elastic modulus parameters (shear and bulk moduli), and nuclear magnetic porosity data for the construction of a multi-dimensional linear equation system, with calibration coefficients derived from core X-ray diffraction (XRD) data. The Adam algorithm dynamically optimizes the weights, solving the overdetermined equation system. We applied the method to the Asmari Formation in the M oilfield in the Middle East with 40 core samples for calibration, achieving a 0.91 fit with the XRD data. For eight additional uncalibrated samples from Well A, the fit reaches 0.87. With the introduction of the elastic modulus and nuclear magnetic porosity, the average relative error in mineral content decreases from 9.45% to 6.59%, and that in porosity estimation decreases from 8.1% to 7.1%. The approach is also scalable to elemental logging data, yielding inversion precision comparable to that of commercial software. Although the method requires a complete set of logging data and further validation of regional applicability for weight parameters, in future research, transfer learning and missing curve prediction could be incorporated to enhance its practical utility. Full article

Conveyance tube manufacturing via a hot-finished, welded route is an energy-intensive process that promotes the rapid surface oxidation of curved surfaces. Previous studies have used computational and theoretical techniques to assess the oxidation of curved surfaces. However, experimental techniques for assessing the oxidation of curved surfaces, as well as for validating existing computational and analytical studies, have significant limitations that impact their ability to accurately recreate industrial processes. The challenges of thermogravimetric analysis (TGA) for in situ tests for the oxidation of cylindrical geometries were investigated, using an as-welded conveyance tube, and compared to an equivalent tube normalised in industry as well as computational predictions for the same geometry and thermal conditions. A core element of this work was the use of a refractory dummy sample to quantify thermal buoyancy and flow-induced vibration. There was a strong agreement between the oxide mass gain predicted by a computational model compared to that of the TGA sample, with only a 5% discrepancy. However, oxide thickness gain, measured using electron microscopy, showed poor agreement, particularly when comparing industrial and experimental results. This was attributed to the need for further work to account for transient heating, oxide porosity, atmospheric composition variation, and the effect of thermomechanical operations during conveyance tube manufacturing, e.g., hydraulic descaling. Full article

Activated carbons (ACs) are employed in the nuclear industry to mitigate the emission of potential radioactive iodine species. Their retention performances towards iodine are mainly dependent on the relative humidity due to the competitive effect induced by adsorbed water molecules. Thus, this work will focus on the prediction of AC behavior toward the capture of water vapor to better assess the poisoning effect on radiotoxic iodine removal. For the first time, H₂O breakthrough curves (BTCs) on nuclear grade ACs are predicted through a specific methodology based on the combination of transport phenomena with adsorption kinetics and equilibrium. Three ACs, similar to those deployed in the nuclear context, are considered within the present study. Our model is based on the Linear Driving Force Model (LDF), governed by an intraparticle diffusion mechanism, notably surface and Knudsen diffusions. In addition, the type V isotherms obtained for H₂O and the investigated carbon supports were described through the Klotz equation, taking into account the formation and progressive growth of H₂O clusters within the internal porosity. This methodology allowed us to successfully simulate the H₂O adsorption by a non-impregnated AC, where only physisorption phenomena are involved. In addition, promising results were highlighted when extrapolating to the two other impregnated ACs (AC 5KI and AC Nuclear). Full article

The Loess Plateau of China, a region highly vulnerable to erosion and climatic variability, faces significant soil degradation exacerbated by intensive agricultural practices and anthropogenic pressures. This study investigates the impacts of incremental soil compaction (P1–P5) on hydraulic properties, pore structure, and water retention across distinct soil textures (sandy loam, loam, clay loam) to address gaps in understanding texture-specific resilience and soil organic carbon (SOC) interactions. Utilizing X-ray computed tomography (CT), soil water retention curve (SWRC) analysis, and the van Genuchten (vG) model, we quantified compaction-induced changes in porosity, connectivity, and hydraulic conductivity, while comparing unsaturated hydraulic conductivity (Kun) predictions derived from mini disc infiltrometer (MDI) and SWRC parameters. Results revealed that fine-textured, SOC-rich soils had greater compaction, preserving macropore connectivity and saturated hydraulic conductivity (Ks), whereas sandy soils pronounced macropore collapse. Compaction homogenized pore distributions, steepened SWRC, and reduced plant-available water. Integration of CT and SWRC methodologies highlighted CT sensitivity to air-filled macropores versus SWRC’s focus on water-retentive micropores. Strong correlation (R² = 0.94–0.99) between vG parameters from MDI and SWRC validated parameter robustness, though MDI slightly underestimated Kun in clay loam, while SWRC-based models aligned closely with observed data. Integrating CT and SWRC methodologies offers a framework for precision soil health monitoring. In addition to the critical role of SOC and texture in compaction mitigation, there is a need for organic amendments in sandy soil and reduced tillage. Full article

Low-permeability sandstone reservoirs have low permeability, but due to their high porosity and difficulty in development, the development difficulty is relatively high. They can fully tap into the high potential of oil and gas resources in low-permeability sandstone reservoirs and occupy an important position in the global energy supply The study area belongs to low-permeability dense sandstone reservoir, and the destination layer has complex lithology, strong physical inhomogeneity, and complicated pore–permeability relationship, so the conventional core pore–permeability regression method and NMR SDR method do not satisfy the requirements of fine evaluation in terms of the accuracy of permeability calculation. According to the principle of resistivity measurement by electromagnetic waves with Logging While Drilling (LWD), this paper analyzes the reasons for the magnitude of resistivity divergence with Logging While Drilling at different exploration depths. There is a “low invasion phenomenon” during the drilling process of the drill bit. The higher the permeability of the formation, the more severe the “low invasion phenomenon”, and the greater the magnitude of resistivity divergence. In this paper, through the conventional log curve response characteristics and correlation analysis, the P40H/P16H parameter were selected to characterize the magnitude of resistivity divergence, and a fine evaluation model of the reservoir based on the P40H/P16H parameter was established in the study area by relying on the theory of the flow unit, and was applied to the prediction of permeability of new wells. The application results show that the calculated permeability is in good agreement with the results of core analysis, which provides a theoretical basis for the fine evaluation of low-permeability tight reservoirs. Full article

Carbonate reservoirs are widely distributed and have great exploration potential. As a key indicator for reservoir characterization and evaluation, accurate and efficient porosity prediction is crucial for the exploration and development of oil and gas in carbonate reservoirs. To address the issues of low prediction accuracy and weak generalization ability in carbonate reservoir porosity prediction, a porosity prediction model (CNN-BiLSTM-Transformer) combining a convolutional neural network (CNN), bidirectional long short-term memory network (BiLSTM), and Transformer network is proposed. This model is applied to the Moxi gas field in the Sichuan Basin, using conventional logging curves as input feature variables for porosity prediction. Root mean square error (RMSE), mean absolute error (MAE), and the coefficient of determination (R²) are used as evaluation metrics for comprehensive analysis and comparison. The model extracts curve features through the CNN layer, captures both short- and long-term neighborhood information via the BiLSTM layer, and utilizes the Transformer layer with a self-attention mechanism to focus on temporal information and input features, effectively capturing global dependencies. The Adam optimization algorithm is employed to update the network’s weights, and hyperparameters are adjusted based on feedback from network accuracy to achieve precise porosity prediction in highly heterogeneous carbonate reservoirs. Compared with traditional machine learning and deep learning models, the improved model better captures domain-specific information, resulting in an R² increase of 0.23 and reductions in RMSE and MAE by 0.016 and 0.014, respectively. Experimental results show that the porosity prediction model based on the CNN-BiLSTM-Transformer algorithm achieves lower average relative error and better prediction performance. Therefore, the CNN-BiLSTM-Transformer model can effectively predict the porosity of carbonate reservoirs and offers valuable insights for carbonate reservoir parameter prediction. Full article

A large-sized backward-curved fan with high shape difficulty was designed, and fan performance was roughly predicted from computational fluid dynamics. Three gating systems of aluminum sand casting were designed to fabricate the fan. The flow pattern and solidification process of molten metal were analyzed by casting simulation. Three types were applied: bottom-up with four gates, bottom-up with ten gates, and top-down with a feeder. The simulation results of the bottom-up with four gates show that a large temperature loss occurs while molten metal flows into thin blades, and there is a temperature range below the liquidus temperature. Due to nonuniform temperature distribution, the solidification pattern is also not uniform. The bottom-up with ten gates shows almost similar flow and solidification patterns but has the effect of slightly reducing the temperature loss of molten metal. The top-down type has a much smaller temperature loss, while molten metal flows into the mold cavity compared to the bottom-up type and has a directional solidification pattern. As the feeder also acts as a riser to compensate for the shrinkage of the thick part, the simulation results regarding porosities are also significantly reduced. The fan cast as a top-down type has soundness without any unfilled parts. Full article

This study investigated the impact of reflective cracking on the fatigue performance of asphalt pavements after milling and resurfacing under various conditions. Fatigue life was assessed through four-point flexural fatigue tests, while the crack extension pattern of composite beams was analyzed by digital image correlation (DIC) at both macroscopic and microscopic scales. Evaluation parameters such as stress ratios, immersion time, porosity, and types of viscous oils were assessed. A fatigue life prediction model of composite beams was established, accounting for the combined influence of these factors. To enhance the accuracy of determining composite beam failure, the critical fatigue damage was calculated by defining the damage variable in terms of the dynamic modulus. A nonlinear fatigue damage model was proposed, incorporating this critical damage under the combined influence of various factors. Additionally, a modified logistic function model was developed to describe the relationship between crack extension and failure life under different stress ratios, porosities, and viscous layer oil conditions. It was found that the modulus decay curves and the crack extension curves intersected at different stress levels as the life ratio increased. At the intersection, the modulus ratios were consistently around 0.55, marking the transition of the specimen from a stable to an unstable state. Beyond this point, the crack rapidly propagated, leading to a sharp reduction in the modulus until the specimen ultimately failed. Our results provide a basis for timing and conservation decisions. Full article

Due to complex geological structures and potential environmental impacts, single-well production in coal-measure gas reservoirs is not satisfactory. Field studies have shown that co-production is a promising approach, which can efficiently and economically extract multiple gas resources. However, the literature lacks a mathematical model to accurately describe and predict the production behavior during co-production. Based on the five-linear flow model, this work presents an analytical solution to evaluate the production dynamics characteristics of co-production between coalbed methane and tight gas reservoirs. In addition, the proposed model accounts for factors such as dual-porosity media, the gas slippage effect, and the matrix shrinkage effect. With the aid of the model, sensitivity analyses of the Blasingame decline curve and the layered flux contribution are conducted. The calculation results show that a higher fracture conductivity, as well as a longer fracture length, lead to larger cumulative production. Additionally, increased layer thickness significantly boosts flux contribution throughout the production period. Finally, large boundary distances extend the duration of high flux contributions in late production. This research contributes to a better understanding of the production dynamics in coal-measure gas reservoirs and offers practical guidelines for reservoir management in co-production scenarios. Full article

Externally applied stress on the rock matrix plays a crucial role in oil recovery from naturally fractured tight reservoirs, as local variations in pore pressure and in-situ tension are expected. The published literature severely lacks in evaluations of the characteristics of hydrocarbons, displaced by water, in fractured reservoirs under the action of externally applied stress. This study intends to overcome this knowledge gap by resolving complex time- and stress-dependent multiphase flow by employing a coupled Finite Element Method (FEM) and Computational Fluid Dynamics (CFD) solver. Extensive three-dimensional numerical investigations have been carried out to estimate the effects of externally applied stress on the multiphase flow characteristics at the fracture–matrix interface by adding a viscous loss term to the momentum conservation equations. The well-validated numerical predictions show that as the stress loading increases, the porosity and permeability of the rock matrix and capillary pressure at the fracture–matrix interface decrease. Specifically, matrix porosity decreases by 0.13% and permeability reduces by 1.3% as stress increases 1.5-fold. Additionally, stress loading causes a decrease in fracture permeability by up to 29%. The fracture–matrix interface becomes more water-soaked as the stress loading on the rock matrix increases, and thus, the relative permeability curves shift to the right. Full article

The Chang 6 sandstone reservoir of the Upper Triassic Yanchang Formation in the Ordos Basin is one of the tight-oil-rich intervals in the basin. Owing to the strong heterogeneity and complex lithology of the Chang 6 reservoir, lithology and fluid identification have become more challenging, hindering exploration and development. This study focused on the Chang 6 member in the Qingcheng area of the Ordos Basin to systematically analyze the lithology, physical properties, and oil-bearing properties of the Chang 6 reservoir. We adopted the method of normalized superposition of neutron and acoustic time-difference curves, the method of induced conductivity–porosity–density intersection analysis, the method of superposition of difference curves (Δφ), and the induced conductivity curve. Our results indicated that the method of normalized superposition of neutron and acoustic wave time-difference curves could quickly and effectively identify the lithologies of tight fine sandstone, silty mudstone, mudstone, and carbonaceous mudstone. The induced conductivity–porosity–density cross-plot could be used to effectively identify oil and water layers, wherein the conductivity of tight oil layers ranged from 18 to 28.1 mS/m, the density ranged from 2.42 to 2.56 g/cm³, the porosity was more than 9.5%, and the oil saturation was more than 65%. Based on the identification of tight fine sandstone using the dual-curve normalized superposition method, the oil layer thickness within the tight fine sandstone could be effectively identified using the superposition of difference curves (Δφ) and induced conductivity curves. Verified by oil-bearing reservoir data from the field test, the overall recognition accuracy of the plots exceeded 90%, effectively enabling the identification of reservoir lithology and fluid types and the determination of the actual thickness of oil layers. Our results provide a reference for predicting favorable areas in the study area and other tight reservoirs. Full article

Porosity assessment is a vital component for reservoir evaluation in the oil and gas sector, and with technological advancement, reliance on conventional methods has decreased. In this regard, this research aims to reduce reliance on well logging, purposing successive machine learning (ML) techniques for precise porosity measurement. So, this research examines the prediction of the porosity curves in the Sui main and Sui upper limestone reservoir, utilizing ML approaches such as an artificial neural networks (ANN) and fuzzy logic (FL). Thus, the input dataset of this research includes gamma ray (GR), neutron porosity (NPHI), density (RHOB), and sonic (DT) logs amongst five drilled wells located in the Qadirpur gas field. The ANN model was trained using the backpropagation algorithm. For the FL model, ten bins were utilized, and Gaussian-shaped membership functions were chosen for ideal correspondence with the geophysical log dataset. The closeness of fit (C-fit) values for the ANN ranged from 91% to 98%, while the FL model exhibited variability from 90% to 95% throughout the wells. In addition, a similar dataset was used to evaluate multiple linear regression (MLR) for comparative analysis. The ANN and FL models achieved robust performance as compared to MLR, with R² values of 0.955 (FL) and 0.988 (ANN) compared to 0.94 (MLR). The outcomes indicate that FL and ANN exceed MLR in predicting the porosity curve. Moreover, the significant R² values and lowest root mean square error (RMSE) values support the potency of these advanced approaches. This research emphasizes the authenticity of FL and ANN in predicting the porosity curve. Thus, these techniques not only enhance natural resource exploitation within the region but also hold broader potential for worldwide applications in reservoir assessment. Full article