Pit Volume Research Articles

Pit volume, soil cover and Eucalyptus forestry residues determine plant growth in restoring areas after gravel mining in eastern Amazon, Brazil

Pit volume, soil cover and Eucalyptus forestry residues determine plant growth in restoring areas after gravel mining in eastern Amazon, Brazil

Experimental research on the erosion characteristics of organ-pipe cavitating jet for hydrate-bearing sediments

Cavitating jet is regarded as a promising breaking rate improvement technology in both the marine natural gas hydrate (NGH) fluidization exploitation method and the integrated radial jet drilling and completion method. Experimental studies on the influences of standoff distance, erosion time, inlet flow rate and hydrate saturation on hydrate-bearing sediments (HBS) breakage by organ-pipe cavitating jet (OPCJ) were conducted. The methane HBS synthesized in laboratory were employed as the experimental samples. The breaking effect were evaluated by four quantifiable indicators: erosion pit volume and maximum depth, and jet specific energy and specific cutting energy. Results show that under the same inlet flow rate, the standoff distance and erosion time have significant influences on the erosion pit shape. For the erosion pit volume and depth, the optimal standoff distances are respectively 4 mm and 5 mm (the diameter of nozzle is 1 mm), in which condition the OPCJ efficiencies of erosion and penetration on HBS are greatly improved. The ultimate erosion depth is 215 mm at an inlet flow rate of 0.085 L/s. The critical breaking velocities of OPCJ and conical jet (CJ) on HBS with hydrate saturation of 50% are respectively estimated to be 17.8 m/s and 24.2 m/s. Increasing hydrate saturation significantly decreases the erosion efficiency of OPCJ on HBS. The inlet flow rate is the most significant factor for the erosion pit volume and depth. This study provides a reference on the potential field applications for the NGH fluidization exploitation method and the integrated radial jet drilling and completion method.

A novel optimization method for geological drilling vertical well

Geological drilling is an important means for exploration of the earth. Due to the nonlinearity and coupling, geological drilling is accompanied by low efficiency and safety, and difficult to accurately predict the drilling states. Moreover, the vertical well trajectory needs to follow the plumb line. A novel optimization method is proposed to solve these problems. First, working conditions are identified by fuzzy C-means clustering method and corresponding adjustment ranges of operating variables are refined for optimization. Meanwhile, support vector regression and hybrid bat algorithm are employed to construct reliable models for mud pit volume (MPV) and rate of penetration (ROP). Then, how to improve safety and efficiency is converted to a two-objective problem, that is to suppress MPV fluctuations and improve ROP with vertical well constraints. Nondominated sorting genetic algorithm II is invoked to solve this problem, and the appropriate solution is selected by two references points. Moreover, a short-time scale and long-time scale optimization strategy is introduced to cope with different adjustment interval of operating variables. Finally, the simulation results based on actual drilling data, application results in a semi-physical experiment system and comparison results from a vertical well verify the effectiveness and practicability for the developed method.

Lost circulation monitoring using bi-directional LSTM and data augmentation

In order to improve the lost circulation risk recognition accuracy of artificial intelligence (AI) based models using limited number of data samples, a lost circulation monitoring method involving data augmentation and Bi-directional Long Short-Term Memory (Bi-LSTM) network is proposed. Firstly, the collected lost circulation data samples including pit volume (PIT), flow-out rate (FOR), pump speed (PS) and standpipe pressure (SPP) data sequences as elements are augmented using percentage scaling and random dithering to produce a dataset with increased number of samples. Then, a Bi-LSTM-based lost circulation monitoring model, which can explore both the past and future information of the input data, is established and trained with the augmented dataset. Finally, the obtained lost circulation monitoring model is applied to the PIT, FOR, PS, and SPP field data sequences for risk monitoring. A collected field lost circulation dataset with 2000 sample points was used to train and test the recognition performance of the proposed method, the test results demonstrate that the recognition accuracies of the LSTM model and Bi-LSTM model without data augmentation are 84% and 89%, respectively. After data augmentation is applied, the recognition accuracy of the Bi-LSTM model is improved to 93%.

Crater effects of shallow burial explosions in soil based on SPH-FEM analysis

This study analyzed the effects and influencing factors of shallowly-buried explosions in soil based on the design and implementation of static explosion experiments and numerical simulations. Based on the static explosion test data, a numerical analysis model of SPH-FEM was established and the explosion process and pit parameters in explosions shallowly buried in soil were analyzed. The results of numerical calculations and comparisons verified the effectiveness of the SPH-FEM method in simulating shallowly-buried explosions in soil. Furthermore, the similarities and differences between the SPH-FEM and ALE methods in the numerical simulation of the same explosion in the soil were assessed. The relationships between the blasting pit radius and explosive depth, and between the explosives weight and pit volume were analyzed. The empirical curve formula of the explosive depth and the crater radius with 9.35 kg of TNT explosive were obtained by mathematical fitting. The results showed that the SPH-FEM method was more refined and more computationally efficient than the ALE method to simulate shallow burial explosions in soil. The established empirical curve formula, which expressed the relationship between the explosive burial depth and the pit radius, effectively predicted the pit radius of the shallow burial explosion. With increasing explosive burial depth, the pit radius increased to the peak value and then decreased rapidly.

Bipolar electrochemistry for high throughput screening of localised corrosion in stainless steel rebars

The corrosion performance of stainless steel rebars is assessed by using bipolar electrochemistry. This method produces a quasi-linear potential gradient across the sample, with the technique providing rapid access to test for the nucleation and growth of pits across a wide range of applied potentials. The localised corrosion behaviour and resulting growth kinetics of Types 304L and 316L austenitic stainless steels (ASSs) are compared to Type 2101 and 2205 duplex stainless steels (DSSs). The difference in selective phase dissolution between pitting and trans-passive corrosion in DSSs is compared, with critical pitting potential, pit nucleation numbers, growth kinetics, volume loss, and aspect ratio of these four stainless steels discussed.

Osteoblast-Mediated Resorption of Porous Bioactive SCPC Granules Enhances Bone Regeneration in Human Extraction Sockets

Bone graft materials are widely used in orthopedic and maxillofacial surgeries. The controlled resorbability of the graft material is essential for bone regeneration. Hydroxyapatite and biphasic calcium phosphate bone grafts have poor resorption and limited bone conductive effects. Histology analyses of bone biopsy from SCPC grafted human extraction sockets showed complete bone regeneration and graft resorption in absence of osteoclasts and macrophages. The hypothesis of the present study is that bioactive SCPC inhibits osteoclast’s activity due to the presence of resorbable silica phase in the material. Our objective is to analyze the effect of SCPC dissolution products on the resorption activity of osteoclasts. The conditioned medium was prepared by immersion of SCPC resorbable bioactive SCPC porous granules (Shefabone, Inc, USA) in cell culture medium at various ratios at 37°C for 3 days. The concentration of Si ions released from the SCPC granules into cell culture medium was measured using ICP-OES. Osteoclast precursors derived from human bone marrow were seeded on bone slices and cultured in the conditioned medium containing 10% FBS and osteoclast induction factors. Osteoclast differentiation and resorption were evaluated by TRAP staining and measurement of the volume of resorption pits on the bone slices. Mature multinuclear giant TRAP-positive osteoclasts were observed on the bone substrates after 14 days incubation in control medium containing osteoclast induction factors. In conditioned medium, the number of multinuclear TRAP-positive cells was significantly decreased as the concentration of SCPC dissolved silica increased. The dissolution of silica from SCPC into the culture medium correlates well with down regulation of osteoclast differentiation and the rapid bone regeneration in human bone defects.

Quantitative 3D Characterization for Kinetics of Corrosion Initiation and Propagation in Additively Manufactured Austenitic Stainless Steel.

In situ X-ray computed tomography (X-ray CT) is used to investigate the effects of characteristic microstructural features on the pitting initiation and propagation in austenitic stainless steel specimens prepared with laser powder bed fusion (LPBF) additive manufacturing. In situ X-ray CT in probing the mechanism and kinetics of localized corrosion is demonstrated by immersing two LPBF specimens with different porosities in an aggressive ferric chloride solution for the evaluation of corrosion. X-ray CT images are acquired from the specimens after every 8hours of immersion over an extended period of time (216hours). Corrosion pit growth is then quantitatively analyzed with a data-constrained modeling method. The pitting growth mechanism of LPBF stainless steel is found to be different from that of conventional stainless steels. More specifically, the mechanism of corrosion pit initiation is closely correlated with the original lack of fusion porosity (LOF) distribution on the surface of the specimens and preferential pit propagation through the LOF pores inside the specimens. Pit growth kinetics are derived from pit volume changes determined through 3D data analysis. The pit growth kinetics in LPBF specimens are found to vary in the initial pit formation, competitive pit propagation, and the dominant pit growth stages.

Effect of substrate orientation and anisotropic strength on corrosion pits

The complexities of pitting corrosion and its associated pit morphologies are critical for predicting and preventing pit-related failure in materials. With the advances in computational tools, modeling of pitting corrosion has lately gained momentum. We recently developed a phase field model for 3-D pit morphology, and in the current work, the model is utilized to gain insight into the role of substrate orientation and anisotropic strength on crystallographic pits. The influence of these parameters on pit morphology and corrosion metrics such as pit volume, current, pit area, current density, and pit depth are studied. The crystallographic pit morphology changes with substrate orientation but not significantly with anisotropic strength. The anisotropic strength strongly influences the corrosion metrics whereas most corrosion metrics remain unaffected by substrate orientation.

Lunar Pit Morphology: Implications for Exploration

AbstractLunar pits are small (∼10–300 m wide) collapse features with vertical walls and sometimes overhangs. We have identified almost 300 pits, mostly in ponds of cooled impact melt inside large craters younger than ∼1 billion years. Several of these pits may provide access to lava tubes or other caves, and those in the maria expose the layering record of the top 20–100 m of basaltic lava flows. We investigated the 21 known pits outside of impact melt ponds to determine possible origins, ages, and present‐day access to the lunar subsurface. We used Lunar Reconnaissance Orbiter Narrow Angle Camera images (<2 m per pixel) to produce detailed 3D reconstructions of six pit interiors. We evaluated the general morphology and geologic context of all twenty‐one. While four pits have contexts suggestive of lava tubes, the majority are ambiguous, although all occur in or near the maria. We also propose that pit formation is an ongoing process, as the degree of degradation of a pit is unrelated to the age of the host terrain. Much of the original volume of most pits is now filled with debris, but some exhibit significant overhangs and may have present‐day cave access. Viewing pit walls and floors, even from the rims, requires navigating steep slopes of loose material. Since the floors are also covered with rough debris piles, we recommend simple flying vehicles for initial reconnaissance.

Central Macular Topographic and Volumetric Measures: New Biomarkers for Detection of Glaucoma.

PurposeTo test the hypothesis that newly developed shape measures using optical coherence tomography (OCT) macular volume scans can discriminate patients with perimetric glaucoma from healthy subjects.MethodsOCT structural measures defining macular topography and volume were recently developed based on cubic Bézier curves. We exported macular volume scans from 135 eyes with glaucoma (133 patients) and 155 healthy eyes (85 subjects) and estimated global and quadrant-based measures. The best subset of measures to predict glaucoma was explored with a gradient boost model (GBM) with subsequent logistic regression. Accuracy and area under receiver operating curves (AUC) were the primary metrics. In addition, we separately investigated model performance in 66 eyes with mild glaucoma (mean deviation ≥ –6 dB).ResultsAverage (±SD) 24-2 mean deviation was –8.2 (±6.1) dB in eyes with glaucoma. The main predictive measures for glaucoma were temporal inferior rim height, nasal inferior pit volume, and temporal inferior pit depth. Lower values for these measures predicted higher risk of glaucoma. Sensitivity, specificity, and AUC for discriminating between healthy and glaucoma eyes were 81.5% (95% CI = 76.6–91.9%), 89.7% (95% CI = 78.7–94.2%), and 0.915 (95% CI = 0.882–0.948), respectively. Corresponding metrics for mild glaucoma were 84.8% (95% CI = 72.1%–95.5%), 85.8% (95% CI = 87.1%–97.4%), and 0.913 (95% CI = 0.867–0.958), respectively.ConclusionsNovel macular shape biomarkers detect early glaucoma with clinically relevant performance. Such biomarkers do not depend on intraretinal segmentation accuracy and may be helpful in eyes with suboptimal macular segmentation.Translational RelevanceMacular shape biomarkers provide valuable information for detection of early glaucoma and may provide additional information beyond thickness measurements.

Application of Machine Learning Methods in Modeling the Loss of Circulation Rate while Drilling Operation.

Fluid losses into formations are a common operational issue that is frequently encountered when drilling across naturally or induced fractured formations. This could pose significant operational risks, such as well control, stuck pipe, and wellbore instability, which, in turn, lead to an increase in well time and cost. This research aims to use and evaluate different machine learning techniques, namely, support vector machine (SVM), random forest (RF), and K nearest neighbor (K-NN) in predicting the loss of circulation rate (LCR) while drilling using solely mechanical surface parameters and interpretation of the active pit volume readings. Actual field data of seven wells, which had suffered partial or severe loss of circulation, were used to build predictive models with an 80:20 training-to-test data ratio, while Well No. 8 was used to compare the performance of the developed models. Different performance metrics were used to evaluate the performance of the developed models. The root-mean-square error (RMSE) and correlation coefficient (R) were used to evaluate the performance of the models in predicting the LCR while drilling. The results showed that K-NN outperformed other models in predicting the LCR in Well No. 8 with an R of 0.90 and an RMSE of 0.17.

Pit volume estimation on the stainless steel according to single current transient

Pit volume estimation on the stainless steel according to single current transient

Are liming and pit size determining for tree species establishment in degraded areas by kaolin mining?

Mining contributes to the global economy on different scales and plays a fundamental role in the development of the goods and services sectors. However, the negative impacts caused by the activity are unavoidable, as they intensely degrade soil structures and modify landscapes. The use of native tree species has been effective in restoring the structure and functions of post-mining ecosystems in the Amazon. Thus, the objective of this work was to evaluate chemical indicators of soil quality, survival and initial growth of six tree species planted in degraded ecosystem by kaolin mining under the effects of liming in three pit volumes in the Eastern Amazon. The initial conditions indicated highly degraded soil, with acidic pH, low content of OM, P and K. Liming significantly reduced the level of toxic Al in the soil and provided Ca and Mg for the plants. Through Principal Component Analysis (PCA), we found that the two first components explained 69.30% of the variance of 13 functional indicators of soil quality. PC1 was positively correlated with Ca, Mg, K, SB, CEC and V% and negatively correlated with H+Al and Al saturation. Survival was higher than 80% for Clitoria fairchildiana in all treatments. Liming and pit volume were determining in growth, with high growth rates in height of Inga edulis, Inga cayennensis, Clitoria fairchildiana and Tachigali vulgaris. The species used in this study presented good initial development in restoring ecosystems after kaolin mining.

A novel modeling and drilling optimization method with suitable constraints in geological well

Drilling is of great importance in developing resources. Rate of penetration (ROP) and mud pit volume (MPV), which reflect drilling efficiency and safety, need to be predicted accurately and maintained in appropriate ranges. Due to the complexity and coupling in the drilling process, drilling safety and efficiency are difficult to improve simultaneously and reliable nonlinear prediction models also need to be constructed. Moreover, the values of optimization objectives are needed to manipulated in proper ranges. This paper presents a modeling and optimization method to solve these problems. In the modeling part, a novel method based on hybrid bat algorithm (HBA) and support vector regression (SVR) is developed to build prediction models, one for predicting ROP and the other for MPV. Then, the objectives intervals are obtained by the fuzzy comprehensive evaluation method, which analyzes the influences of multiple variables. Finally, a novel drilling optimization method is developed to improve ROP and reduce MPV fluctuations according to the objectives intervals and nondominated sorting genetic algorithm II (NSGA-II). The simulation results indicate that prediction models constructed by the developed method have superior performance than other modeling methods. Moreover, the developed method can improve ROP by an average of 35.8% and reduce the fluctuations of the MPV by an average of 34.3%, which means the developed method can improve safety and efficiency at the same time. The developed method is also applied in drilling equipment, the running results further illustrate the validity.

Temperature-Dependent Charge Dynamics of Double Layer Interface in 500 kV HVDC XLPE Cable Factory Joint with Different Interfacial Roughness

This article investigates the effect of different temperatures (30 °C, 50 °C, and 70 °C) on the interfacial and electrical insulation properties of 500 kV high voltage direct current (HVDC) cross-linked polyethylene (XLPE) cable factory joint having different roughness levels. The test samples with different interfacial roughness (#80, #400, #1000, #2000, and #0000) were prepared by surface polishing followed by an XLPE injection vulcanization process. At the interface, visible microporous defects were observed. The volume of pits on the sample surface and microporous defects at the interface can be reduced by a smooth interface. Moreover, the smoother interface facilitates space charge accumulation. The dc breakdown strength was nearly the same regardless roughness level at high temperatures. The results show that smooth interfaces help improving insulation properties of cable factory joints at low temperatures. However, the effect of interfacial roughness on insulating properties is weakened at elevated temperatures.

Determination of geometric characteristics of explosive eruptions on agricultural lands using remote methods

The armed aggression of the Russian Federation against Ukraine led to significant damage to the fertile soil layer in the temporarily occupied territories. Damage assessment for agricultural lands damaged by ravines requires determination of their geometric characteristics. There are two groups of methods for finding the area and volume of pits. The first group is contact geodetic research, which allows you to accurately determine the parameters of craters, but it is impossible to apply them due to the explosiveness of the territory and the large number of damaged areas. Other methods are related to the determination of geometric characteristics based on the data of remote sensing of the Earth (RS). They make it possible to quickly identify the location of explosive holes, but in wartime they are not fully available. Therefore, the complex use of remote sensing data and contact research will allow to quickly and safely determine the location and geometric characteristics of explosive craters.The purpose of the study is to develop a methodology for determining the geometric characteristics of explosive craters due to the complex use of contact and remote geodetic data in the conditions of military operations. The main tasks of the research: analysis of existing methods for determining the geometric characteristics of explosive craters; determination of the relationship between the force of the explosion and the geometric characteristics of the eruption; development of a methodology for determining damage to the fertile layer of agricultural lands from military operations using Geoinformation technologies (GIS technologies); practical implementation of the developed methodology on the example of a test plot of agricultural land.The developed technique consists in the use of geo-information technologies and data of RS to determine the location and area of explosive craters. Based on the existing statistical relationships between the TNT equivalent of the explosion and the geometric characteristics, new functions of the relationship between the radius of the rupture and the volume, depth, and TNT equivalent of the explosion were obtained.Cartographic models of the distribution of craters by volume, the density of explosive craters and their distribution by radius were built in the ArcGIS geoinformation software.The constructed geomodels made it possible to assess the degree of damage to agricultural soils and to determine the most explosive areas. Based on the obtained data, it was established that there is no correlation between the density of the holes and their radius in the presence of holes larger than 25 m.Assessment of damage to agricultural land as a result of military operations in Ukraine requires the use of images from UAVs and satellites with ultra-high spatial resolution. In the future, it is necessary to check the adequacy of the developed methodology by field geodetic methods.

Lost circulation prediction based on machine learning

Lost circulation, one of the most headache problems in drilling engineering, causes a significant increase in non-productive time and increases the uncertainty of well control risk. There is a tremendous challenge for lost circulation prediction/diagnosis using traditional methods in carbonate formation due to its complex and variable leakage zone. To solve this problem of the Mishrif reservoir in the H oil field, in this paper, we propose a whole practical workflow of lost circulation prediction based on the Mixture Density Network using deep learning. Firstly, we choose 16 comprehensive mudlogging parameters that are the most correlated with the mud loss rate. These parameters are selected from a total of 22 comprehensive mudlogging parameters using three different feature selection approach. Then, combining the parameters and the mud loss rate, we get the relationship between comprehensive mudlogging parameters and the mud loss rate based on the Mixture Density Network including five sub-Gaussian models. Finally, the mud loss rate distribution is obtained according to the relationship with input parameters in real-time. The reliability is also evaluated using the uncertainty information in the model. The results show that the parameters had the highest correlation with the mud loss rate, including Measure Depth, Vertical Depth, Rate of Penetration, Hook load, Pump Pressure, Stroke Per Minute, Flow In, Flow Line, Temperature In, Temperature Out, Mud weight In, Mud weight Out, Conductivity Out, Equivalent Circulating Density, Total Gas and Pit Volume Total. The Mixture Density Network has a solid ability to describe data suitable for mud loss prediction and diagnosis. Improving the quality of training data is very important to improve the prediction accuracy of the model. Due to uncertainty in the weights of the sub-Gaussian model obtained from training, the final probability density distribution curve may be single or multi-peaked. According to the prediction results, lost circulation can be controlled by the corresponding method. Thus, the workflow not only allows for real-time assessment of lost circulation risk during drilling but also provides a reference for optimizing lost circulation control.

Column bio-oxidation of low-grade refractory gold ore containing high-arsenic and high-sulfur: Insight on change in microbial community structure and sulfide surface corrosion

Currently, there is a lack of research on column bio-oxidation for low-grade refractory gold ore with high arsenic and sulfur content. Therefore, it is essential and significative to investigate column bio-oxidation for this type of gold ore. The column bio-oxidation process of refractory gold ore was characterized by a variety of analytical methods, including X-ray diffraction, 16S rRNA gene analysis, scanning electron microscope coupled with energy-dispersive X-ray spectroscopy and 3D confocal laser scanning microscopy. The evolution of the microbial community structure was analyzed. Simultaneously, the process of passive film formation and the behavior of the dynamic corrosion on the sulfide surfaces were described in detail. The results showed that the cyanidation of gold yielded 81 ± 2% after column bio-oxidation. The oxidation extent for As, Fe and S were 57 ± 1%, 38 ± 1% and 44 ± 1%, respectively. The community structure of attached and planktonic microorganisms was different and changed with the oxidation time. Importantly, the Sulfur film was observed on the arsenopyrite surface under scanning electron microscope, yet it was not found on the pyrite surface. This finding revealed that the inhibitory effect generated by the passive film of arsenopyrite was significantly higher than that of pyrite. Furthermore, the average corrosion pit volume per unit area of pyrite and arsenopyrite were 161 ± 6 μm3/μm2 and 65 ± 3 μm3/μm2. This indicated that arsenopyrite was more easily oxidized during the column bio-oxidation.

Assessing Foveal Structure in Individuals with TYR R402Q and S192Y Hypomorphic Alleles.

PurposeTo assess the impact of two TYR hypomorphic alleles (R402Q and S192Y) on foveal pit and foveal avascular zone (FAZ) morphology.DesignProspective, cross-sectional study.ParticipantsA total of 164 participants with normal vision (67 male and 97 female; mean ± standard deviation [SD] age = 30.5 ± 12.8 years) were recruited.MethodsSequencing of more than 100 pigmentation-related genes was performed, and results were reviewed for the presence or absence of the TYR polymorphisms R402Q (rs1126809) and S192Y (rs1042602). Volumetric scans of the macula were obtained for each participant using OCT, and retinal thickness maps were analyzed using custom software. OCT angiography was used to image the FAZ, which was manually segmented and measured. Linear mixed model analysis was used to assess associations between genotype and foveal pit morphology.Main Outcome MeasuresFoveal pit depth, diameter, volume, and FAZ area in relation to the presence of hypomorphic alleles R402Q and S192Y on the TYR gene.ResultsHeterozygosity for the TYR R402Q allele was associated with decreased pit diameter (P = 0.0094) and decreased FAZ area (P = 0.025). Homozygosity for the TYR R402Q allele was associated with reduced pit volume (P = 0.0005), decreased pit depth (P = 0.007), reduced pit diameter (P = 0.0052), and reduced FAZ area (P = 0.0012). Homozygosity for TYR S192Y was associated with reduced FAZ area (P = 0.016). Heterozygosity for the TYR S192Y allele was not associated with differences in foveal pit depth, diameter, volume, or FAZ area (P > 0.05).ConclusionsAlthough the role of the TYR R402Q and S192Y hypomorphic alleles in albinism remains controversial, our data suggest that these variants contribute to the extensive inter-individual variability in foveal morphology in the normal population. Our results contribute to the evolving picture of the relationship between ocular pigmentation and foveal morphology.