Arabian Journal for Science and Engineering最新文献

Portland cement concrete (PCC) is the construction material most used worldwide. Hence, its proper characterization is fundamental for the daily-basis engineering practice. Nonetheless, the experimental measurements of the PCC’s engineering properties (i.e., Poisson’s Ratio -v-, Elastic Modulus -E-, Compressive Strength -ComS-, and Tensile Strength -TenS-) consume considerable amounts of time and financial resources. Therefore, the development of high-precision indirect methods is fundamental. Accordingly, this research proposes a computational model based on deep neural networks (DNNs) to simultaneously predict the v, E, ComS, and TenS. For this purpose, the Long-Term Pavement Performance database was employed as the data source. In this regard, the mix design parameters of the PCC are adopted as input variables. The performance of the DNN model was evaluated with 1:1 lines, goodness-of-fit parameters, Shapley additive explanations assessments, and running time analysis. The results demonstrated that the proposed DNN model exhibited an exactitude higher than 99.8%, with forecasting errors close to zero (0). Consequently, the machine learning-based computational model designed in this investigation is a helpful tool for estimating the PCC’s engineering properties when laboratory tests are not attainable. Thus, the main novelty of this study is creating a robust model to determine the v, E, ComS, and TenS by solely considering the mix design parameters. Likewise, the central contribution to the state-of-the-art achieved by the present research effort is the public launch of the developed computational tool through an open-access GitHub repository, which can be utilized by engineers, designers, agencies, and other stakeholders.

In horizontal well operations, the prevalence of stuck pipe incidents is largely attributed to inadequate hole cleaning, underscoring the critical need for a thorough understanding of this process to mitigate non-productive time and financial losses. Increasing fluid velocity, the major drilling parameter of hole cleaning, diminishes the formation of cuttings beds in a wellbore. This study primarily centered on the mechanical displacement and removal of solid particles, employing advanced image processing techniques to elucidate the dynamic behavior of solid particles in deviated wellbores. The core objective of this study was to experimentally scrutinize the effects of a downhole clamp-on tool on fluid velocity to improve hole cleaning practices. To address this challenge, a customized flow loop was designed and constructed to accurately replicate the conditions encountered in horizontal wells. Pure water was used to demonstrate the effects of the clamp-on tool on cuttings transport for lightweight drilling fluid conditions. Strategically deployed, the clamp-on tool played a pivotal role in agitating cuttings, mitigating their accumulation at the bottom of the borehole. The tool's agitation mechanism noticeably improved cuttings removal by increasing velocity, extending the perturbation of cuttings transport in the tool's downstream flow, and reducing bedding formation. At lower flow rates, the tool led to an over fourfold increase in average particle velocity within the tool and a twofold increase after the tool. Our results demonstrate the substantial potential of mechanical assistance to address hole cleaning challenges and significantly advance horizontal well operations.

Stainless steel is a notably beneficial product and is widely used in many applications. Controlling the quality of steel products by determining their trace elemental content is essential. The content of trace elements such as Cr in steel protects the alloy from corrosion and enhances its lifetime, especially in the case of steel embedded in concrete structures. This article presents a quantitative analysis of the trace element Cr in steel samples using advanced laser-induced breakdown spectroscopy (LIBS) with an Nd:YAG laser at wavelengths of 355 and 1064 nm. The LIBS emission intensity of Cr at a laser wavelength of 355 nm was improved compared to that at 1064 nm. Notably, the calibration curves exhibit a zero intercept using the LIBS technique at the third harmonic (355 nm) and fundamental (1064 nm) wavelengths generated by the Nd:YAG laser. The detection limits for Cr in steel were determined as 21 and 36 parts per million using the laser wavelengths of 355 and 1064 nm, respectively. This study demonstrated that the wavelength of 355 nm is significantly more suitable for the quantitative analysis of stainless steel than 1064 nm in laser-induced spectroscopy.

Anchors embedded in soil are used to restrain horizontal movement of structures, especially pipelines. Block anchors are not thoroughly studied in the literature as compared to plate anchors. This research paper intended to address this gap and contribute to the field by studying various parameters influencing the behavior of block anchors embedded in sand when subjected to a horizontal load. The behavior focuses on the pullout capacity and displacements/rotation of the block anchor, and the failure mode of the soil. The parameters studied include width, depth, and thickness of the block; depth of embedment below the ground surface; location of the pulling load; and the degree of saturation of the soil. The rigorous research methodology consists of numerical, analytical, and experimental approaches. First, the analytical calculations were based on Rankine, Coulomb, and log spiral theories to obtain values for the pullout capacity, for the 3-D magnification factor, and for the mobilized friction angle. Second, the experimental work included pullout tests, made in the laboratory, on concrete block anchors of various dimensions and on steel plate anchors, embedded in sand at two different depths. The sand was deposited in a box by pluviation to ensure a uniform and reproducible density. Materials properties were determined, and instruments were calibrated. The load and the corresponding horizontal and vertical displacements were recorded, and visual observations of the failed soil surface were captured. Finally, the numerical computations used PLAXIS program for the 2-D cases to obtain the pullout capacity and the deformation for long anchors. The main findings of this study show that the block anchor has a higher pullout capacity than a plate anchor; and the depth of embedment and the moisture condition of the sand significantly affect the pullout capacity, while the thickness of the block and the exact location of the load do not significantly affect the capacity. The capacity of a short block anchor per unit width decreases with increasing width, as the 3-D effect reduces. With reference to dry sand, the capacity of the anchor is doubled if the sand is unsaturated/wet, but it is reduced to only one half if the sand is saturated. The experimental results were compared with the analytical calculations and also with the numerical computations. The analytical results were also utilized for the experimental design. The results of numerical computations were used to validate the experimental design and to explain experimental findings, especially failure mode and deformation. The findings of this research are also compared with other studies reported in the literature. These findings have very significant implications to the analysis and design of the block anchor. They also contribute to the hazard risk assessment of block anchors embedded in sand subjected to variations in the environmental condition of wetting and drying cycles.

The strength reduction method plays a crucial role in the slope stability analysis. Generally, the Mohr–Coulomb (MC) model is commonly used to analyze slope stability. However, the MC model has the computational nonconvergence problem and the overestimated tensile strength. Usually, the Drucker–Prager (DP) model can be used to approximate the MC model on the ({pi }) plane utilizing a circle, which can improve the computational nonconvergence problem. However, the DP yield surface still has a corner on the meridian plane, which results in computational instability in extreme circumstances and requires special attention. Additionally, the DP model overestimates the tensile strength. Another work is the modified MC model, which can enhance the computational stability and describe the tensile strength reasonably. However, the implementation of the modified MC model is uneasy due to its complex derivatives. For both the DP model and the MC model, another problem is that the tensile strength does not exist explicitly in the yield function and cannot be reduced directly, which restricts their applications. To address these problems, this paper proposes a modified Drucker–Prager (DP) model, which is easy to implement, numerically stable, and capable of adjusting the tensile strength. Moreover, a strength reduction method considering tensile strength reduction is proposed, which is applicable to the proposed modified DP model and the modified MC model. Finally, the numerical tests demonstrate the effectiveness of the proposed methods. These methods serve as a beneficial supplement to the MC and DP models in slope stability analysis.

Drilling deviated wellbores has raised concerns about proper cutting transport. Cuttings settling downhole can create stationary cutting beds, causing drilling mishaps like stuck pipes. High fluid velocity is typically required to efficiently erode a stationary bed, but this is constrained by hydraulic and wellbore geometry. When this occurs, pipe rotation can erode the bed mechanically and enable efficient cutting transport even with lower fluid velocities. Therefore, this study formulated water-based mud (WBM) with hydroxyapatite nanoparticles (n-HAp) to examine the effect of pipe rotation on cutting transport in deviated wells. It was compared with copper II oxide nanoparticles (CuO NP) in terms of rheology, filtration, and cutting transfer efficiency (CTE). The CTE of n-HAp amounts (0.4–2.0 g) in moving cuttings with diameters of 0.80 to 3.60 mm through deviated wellbores of 40 to 65° at a 3.5 m/s fluid velocity with 60 and 120 rpm pipe rotation speeds was determined. Compared with CuO NP, n-HAp findings demonstrated enhanced rheology and CTE. However, for fluid loss control, n-HAp was slightly less effective compared to CuO NP. For all deviated angles, n-HAp increased the CTE by 9.5–50%, while CuO NP increased it by 3.4–38.7% at 120 rpm. Compared with 60 rpm, a higher CTE occurred at 120 rpm. Moreover, CTE occurs in the following manner: 40° > 65° > 45° > 60° > 50° > 55°. It suggests that stationary bed formation is more likely to occur at inclinations of 50–55°. These findings are crucial for drilling deviated wells.

The process of injecting carbon dioxide (CO₂) into oil reservoirs involves intricate interactions between the reservoir’s hydrocarbons and CO₂, resulting in changes to various physiochemical properties such as interfacial tension (IFT) between complex acidic hydrocarbons and CO₂. The IFT plays a crucial role in enhanced oil recovery by altering capillary force. Hydrocarbon mixtures usually contain a percentage of acidic organic compounds that pose a crucial effect on IFT measurement. In this study, benzoic acid was chosen as a representative of acidic compounds to study the effect of acid number (0.0–0.8 g/L) on the IFT between CO₂ and different combinations of diesel fuel (representing aliphatic hydrocarbons) and gasoline (representing aromatic hydrocarbons). The volume ratios of the mixtures were varied at 0:100, 25:75, 50:50, 75:25 and 100:0%. Results revealed that increasing the acid number in gasoline from 0.2 to 0.8 g/L led to a significant change of more than 10 dyne/cm in the IFT between aromatic hydrocarbons and CO₂. On the other hand, IFT alteration for aliphatic hydrocarbons was observed to be up to 5 dyne/cm. A new correlation for estimating IFT between CO₂ and different hydrocarbons was also proposed with an average relative error of less than 9%.

Accurate estimation of formation conditions plays a pivotal role in effectively managing various processes related to hydrates, including flow assurance, deep-water drilling, and hydrate-based technology development. The formation temperature of methane hydrates in the presence of brine greatly affects the efficacy and accuracy of these processes. This work presents a comprehensive and novel comparative analysis of nine distinct machine learning models for accurate prediction of formation temperatures of methane hydrate. This study investigated the application of major machine learning (ML) algorithms including multiple linear regression (MLR), long short-term memory (LSTM), radial basis function (RBF), support vector machine (SVM), artificial neural network (ANN), gradient boosting regression (GBR), gradient process regression (GPR), random forest (RF), and K-nearest neighbor (KNN). The model accuracy was validated against a large dataset comprising of over 1000 data points with diverse range of salt concentrations. In this regard, model accuracies were compared using several metrics including R², ARD, and AARD. The experimental results exhibited KNN algorithm to be fast-converging, accurate, and consistent over the entire range of data points with an R² score of 0.975 and AARD of 0.385%. The results enable efficient and accurate temperature estimation with ML algorithms for multiple hydrate-related processes.

The purpose of this work was to obtain the kinetic model parameters of hydrocracking of crude oil recovered from Ixachi onshore well, México. Recovered crude oil was purified to remove water and sediments, and then, the feedstock was subject of hydrocracking at 395 and 410 °C of reaction temperature, 1000 psi of initial hydrogen pressure, 0.84 wt.% catalyst-to-oil, stirring at 750 rpm, by using a batch reactor of 100 mL. The reaction time was varied from 1 to 4 h for each reaction temperature. 40/60 mesh NiMo supported ZSM-5 catalyst was used, firstly activated in gas phase by using 15 vol% H₂S/85 vol% H₂. The parameter estimation for kinetic modeling of hydrocracking was carried out using a nonlinear algorithm and five lumps. The experimental data demonstrated that, as reaction time increased, the naphtha and kerosene fraction increased, while residue cut was reduced, being the yields higher at 410 °C in any case compared with those yields at 395 °C. The secondary hydrocracking was more evident for light gasoil, and heavy gas oil cuts at the two reaction temperatures. Regarding the kinetic modeling, the overall error of estimation of kinetic parameters was 0.24 and the coefficient of determination of the linear regression of the parity graph was 0.9919 at confidence interval of 95%; the values of the activation energies ranged from 31 to 297 kJ/mol.