Penetration Rate Research Articles

Exposure behavior and drilling efficiency of basalt fiber composite impregnated diamond bits in hard granite

Impregnated diamond bits (IDBs) are widely used for drilling in hard formations. To improve the drilling efficiency and exposure behavior of IDBs in granite, three types of Cu-based basalt fiber (BF) composite IDBs were designed and prepared by using the medium-frequency induction hot-pressing and sintering method, in which 0 wt.% BF and 25 vol.% diamond were used in 0BF25D IDB, 1 wt.% BF and 25 vol.% diamond were used in 1BF25D IDB, 1 wt.% BF and 20 vol.% diamond were used in 1BF20D IDB. The drilling efficiency of each IDB was tested under different drilling pressures (WOB), and the exposure behavior of IDBs was investigated by scanning electron microscopy and ultra field microstructural characterization. Results show that drilling granite with grade 9 drillability, low drilling pressure can drill successfully with the addition of BF. The average rate of penetration (ROP) of 1BF20D IBD under 6 kN WOB was 4.78 m/h, which was improved by 60%∼80%, energy consumption decreased by 66% for each meter, torque (TOB) decreased, and rotational speed (RPM) was more stable during the drilling process. The addition of BF and reasonable diamond concentration enhanced the holding power of the diamond which promoted the exposure of the diamond. The average exposed height of the diamond in 1BF20D IDB reached 121.2 μm with 22% to 29% of the whole diamond.

A rate of penetration (ROP) prediction method based on improved dung beetle optimization algorithm and BiLSTM-SA

In the field of oil drilling, accurately predicting the Rate of Penetration (ROP) is crucial for improving drilling efficiency and reducing costs. Traditional prediction methods and existing machine learning approaches often lack accuracy and generalization capabilities, leading to suboptimal results in practical applications. This study proposes an end-to-end ROP prediction model based on BiLSTM-SA-IDBO, which integrates Bidirectional Long Short-Term Memory (BiLSTM), a Self-Attention mechanism (SA), and an Improved Dung Beetle Optimization algorithm (IDBO), incorporating the Bingham physical equation.We enhanced the DBO algorithm by using Sobol sequences for population initialization and integrating the Golden Sine algorithm and dynamic subtraction factors to develop a more robust IDBO. This optimized the BiLSTM-SA model, resulting in a BiLSTM-SA-IDBO model with an RMSE of 0.065, an R² of 0.963, and an MAE of 0.05 on the test set. Compared to the original BiLSTM-SA model, these metrics improved by 78%, 21%, and 83%, respectively. Additionally, we compared this model with BP Neural Network, Random Forest, XGBoost, and LSTM models, and found that our proposed model significantly outperformed these traditional models. Finally, through practical testing, the model’s excellent predictive ability and generalization were verified, demonstrating its great potential for practical applications.

Asymmetric effects of life and non-life insurance on economic growth in Saudi Arabia: A nonlinear analysis

This paper explores the asymmetric effects of life and non-life insurance on economic growth in Saudi Arabia. Quarterly data on insurance penetration rates and GDP growth from 2009 to 2022, obtained from the Global Economy, Saudi Arabian Monetary Authority, and World Bank databases, are utilized. A nonlinear autoregressive distributed lag (NARDL) model is employed to examine the relationships between insurance and growth. The results reveal a significant and asymmetric relationship between life insurance penetration and GDP growth. Specifically, a 1% increase in life insurance penetration is associated with a 0.3% increase in GDP growth in the long run, while a 1% decrease shows no significant effect. For non-life insurance, both increases and decreases demonstrate significant but asymmetric impacts on growth. A 1% increase in non-life insurance penetration corresponds to a 0.2% increase in GDP growth, whereas a 1% decrease is linked to a 0.15% decrease in GDP growth. These findings support the ‘supply-leading’ hypothesis, suggesting that the insurance sector can play a leading role in promoting economic growth. The results provide new quantitative insights into the relationship between insurance and economic growth in Saudi Arabia, offering valuable implications for policymakers. It is suggested that the insurance industry can be leveraged to foster economic growth by promoting life insurance and managing the asymmetric impacts of non-life insurance.

Experimental and numerical investigation on the relationship between stick–slip vibration and rate of penetration

Experimental and numerical investigation on the relationship between stick–slip vibration and rate of penetration

Liquid Water Transport and Distribution in the Gas Diffusion Layer of a Proton Exchange Membrane Fuel Cell Considering Interfacial Cracks

The proton exchange membrane fuel cell (PEMFC), with a high energy conversion efficiency, has become an important means of hydrogen energy utilization. However, water condensation is unavoidable in the PEMFC because of low operating temperatures. The impact of liquid water on PEMFC performance and stability is significant. The gas diffusion layer (GDL) provides a critical transport path for liquid water in the PEMFC. Liquid water saturation and distribution in the GDL determine water flooding and mass transfer efficiency in the PEMFC. In this study, focusing on the effects of the water introduction method, osmotic pressure, and contact angle, the liquid water transport in the GDL was numerically investigated based on a pore-scale model using the volume of fluid (VOF) method. The results showed that compared with the conventional water introduction method without cracks, the saturation and spatial distribution of water inside the GDL obtained in the simulation were more consistent with the experimental results when the water was introduced through the microporous layer (MPL) crack. It was found that increasing the osmotic pressure resulted in a faster rate of water penetration, faster approaching the steady-state performance, and higher saturation. The ultra-high osmotic pressure contributed to the secondary breakthrough with a significant increase in saturation. Increasing the contact angle caused higher capillary resistance, especially in the region with small pore sizes. At a constant osmotic pressure, as the contact angle increased, the liquid water gradually failed to penetrate into the small pores around the transport path, causing saturation reduction and an ultimate failure to break through the GDL. Increasing the contact angle contributed to a higher breakthrough pressure and secondary breakthrough pressure.

Modeling and Estimating the Effect of a Mix of Varying Levels of Automated Vehicles on Operational Performance of Urban Freeways

Automated vehicles (AVs) will likely influence various aspects of the existing transportation system in the next few decades. Existing literature documents the operational benefits of AVs at different penetration rates. However, estimating the effect of the mix of different levels of AVs on the operational performance of traffic flow has not been explored in the past. This study employs a microsimulation approach to model and estimate the potential effect of varying levels of AVs on the operational performance of freeways. The proposed approach involves a two-step process for calibrating the simulation model. In the first step, the model is calibrated from an operational perspective. In the second step, the calibration is performed from a safety perspective. Thirteen scenarios were generated considering varying penetration rates of different levels of AVs. Congestion index (CI), buffer time (BT), travel time uncertainty (TTU), and delay were computed for each scenario and segment type. The estimations indicate that increasing the penetration of level 3 to level 5 AVs significantly reduces CI, BT, TTU, and delay. In contrast, the effect of level 1 and 2 AVs on freeway operational performance is negligible. Notably, the operational benefits are more pronounced with higher penetration of level 3 and higher AVs. Additionally, the reduction in delay is greater on interchange segments compared with basic freeway segments. The study findings are valuable insights concerning the operational performance of freeways under varying penetration of different levels of AVs, assisting practitioners in formulating AVs-inclusive policies.

Modeling rate of penetration using hybridization of Artificial Neural Network and Artificial Fish Swarm algorithm

This study aims to develop a robust and efficient approach for predicting the Rate of Penetration (ROP) by integrating the Artificial Fish Swarm Algorithm (AFSA) with the Back Propagation (BP) algorithm in an Artificial Neural Network (ANN). The integration of AFSA with Back Propagation is the primary novelty of this work, as it seeks to achieve optimal weight and bias values for the ANN, thereby enhancing prediction accuracy. A dataset of 1944 data points from a vertical well in southern Algeria was utilized. The performance of the developed models was evaluated using correlation coefficient and root mean square error (RMSE). The results demonstrated that the AFSA-ANN model significantly outperforms conventional ANN, Support Vector Regression (SVR), Random Forest regressor (RFR), and Bourgoyne and Young’s model in terms of accuracy. Additionally, the application of leverage method revealed that only 0.98% of the data falls outside the model’s applicability domain, indicating that the data and the model are statistically valid and reliable. The findings of this study have significant implications for the oil and gas field, providing a more accurate and reliable method for predicting ROP, which can lead to more efficient drilling operations and reduced costs.

A smarter approach to liquefaction risk: harnessing dynamic cone penetration test data and machine learning for safer infrastructure

This paper presents a novel approach for assessing liquefaction potential by integrating Dynamic Cone Penetration Test (DCPT) data with advanced machine learning (ML) techniques. DCPT offers a cost-effective, rapid, and adaptable method for evaluating soil resistance, making it suitable for liquefaction assessment across diverse soil conditions. This study establishes a threshold criterion based on the ratio of the penetration rate to the dynamic resistance (e/qd), where values exceeding four indicate high liquefaction susceptibility. ML models, including Support Vector Machine (SVM) optimized with Particle Swarm Optimization (PSO), Grey Wolf Optimizer (GWO), Genetic Algorithm (GA), and Firefly Algorithm (FA), were employed to predict the e/qd ratio using key geotechnical parameters, such as fine content, peak ground acceleration, reduction factor, and penetration rate. The SVM-PSO model demonstrated superior performance, with high R2 values of 0.999 and 0.989 in the training and testing phases, respectively. The proposed methodology offers a sustainable and accurate approach for liquefaction assessment, reducing the environmental impact of geotechnical investigations, while ensuring reliable predictions. This study bridges the gap between field testing and advanced computational techniques, providing a powerful tool for geotechnical engineers to assess liquefaction risks and design resilient infrastructures.

Reproducibility of Cardiac Multifrequency MR Elastography in Assessing Left Ventricular Stiffness and Viscosity.

Cardiac magnetic resonance elastography (MRE) shows promise in assessing the mechanofunctional properties of the heart but faces clinical challenges, mainly synchronization with cardiac cycle, breathing, and external harmonic stimulation. To determine the reproducibility of in vivo cardiac multifrequency MRE (MMRE) for assessing diastolic left ventricular (LV) stiffness and viscosity. Prospective. This single-center study included a total of 28 participants (mean age, 56.6 ± 23.0 years; 16 male) consisting of randomly selected healthy participants (mean age, 44.6 ± 20.1 years; 9 male) and patients with aortic stenosis (mean age, 78.3 ± 3.8 years; 7 male). 3 T, 3D multifrequency MRE with a single-shot spin-echo planar imaging sequence. Each participant underwent two cardiac MMRE examinations on the same day. Full 3D wave fields were acquired in diastole at frequencies of 80, 90, and 100 Hz during a total of three breath-holds. Shear wave speed (SWS) and penetration rate (PR) were reconstructed as a surrogate for tissue stiffness and inverse viscous loss. Epicardial and endocardial ROIs were manually drawn by two independent readers to segment the LV myocardium. Shapiro-Wilk test, Bland-Altman analysis and intraclass correlation coefficient (ICC). P-value <0.05 were considered statistically significant. Bland-Altman analyses and intraclass correlation coefficients (ICC = 0.96 for myocardial stiffness and ICC = 0.93 for viscosity) indicated near-perfect test-retest repeatability among examinations on the same day. The mean SWS for scan and re-scan diastolic LV myocardium were 2.42 ± 0.24 m/s and 2.39 ± 0.23 m/s; the mean PR were 1.24 ± 0.17 m/s and 1.22 ± 0.14 m/s. Inter-reader variability showed good to excellent agreement for myocardial stiffness (ICC = 0.92) and viscosity (ICC = 0.85). Cardiac MMRE is a promising and reproducible method for noninvasive assessment of diastolic LV stiffness and viscosity. 2 TECHNICAL EFFICACY: 1.

Biochar influences phytoremediation of heavy metals in contaminated soils: an overview and perspectives.

Heavy metals (HMs) contamination has gained much attention due to its high degree of toxicity for living organisms. Therefore, different techniques are underway to eradicate HMs from the environment. Among the biological techniques, phytoremediation is a suitable method, but owing to the slow rate and chance of HMs penetration into the food chain, alternative techniques are needed to reduce their phytotoxicity, and biochar is one of them. Due to the diverse characteristics, biochar immobilizes HMs in the soil by improving soil pH, ion exchange, electrostatic interactions, complexation, precipitation, surface adsorption, and microbial activation. Thereby, amendment of biochar in the HMs-contaminated soils reduces HMs toxicity to plants and limits their penetration into the food chain. In contrast, some biochars have also been studied to induce metal availability in soils and subsequently its uptake by plants. This dual role of biochar depends on the feedstock of biochar, incineration temperature, and the rate of application. Moreover, biochar treatments enhance plant growth under HMs stress by improving nutrient availability, water retention capacity, scavenging of reactive oxygen species, and photosynthetic efficiency. Owing to the beneficial characteristics of biochar in HMs-contaminated sites, the number of publications has tremendously increased. Additionally, the plant species and the types of HMs that have been tested frequently under biochar treatments in these articles have been studied. Overall, the current study would help in understanding the mechanisms of how biochar influences phytoremediation of HMs and improves plant growth in HMs-polluted soils and the current scenario of the available literature.

Prediction of TBM cutter wear in heterogeneous ground under high ambient pressure

To prevent tunnel face collapse in heterogeneous ground, the applied face pressure in the excavation chamber can be increased to stabilise the tunnel face, which may hinder cutter rotation during tunnel boring machine (TBM) tunnelling. This study proposes a TBM cutter wear prediction model that considers the impact of the variation in the cutter normal force on the cutterhead owing to the high applied face pressure and low penetration rate. First, the cutter motion mode in heterogeneous ground is investigated. The cutter is found to slide when cutting soft rock based on an analysis of the field data. The evolution process of the cutter wear morphology under heterogeneous ground conditions is summarised. A method for calculating the cutter normal force when the cutter slides on the tunnel face is proposed by referring to the cutting mechanism of the drag bits. The cutter hardness is measured on-site using a portable hardness tester. A semi-empirical model considering the variation in the cutter normal force on the cutterhead is proposed to predict the cutter wear in heterogeneous ground. Wear prediction is compared with other predictive models and field data for validation, and the discrepancies between the different models are discussed. The results show that the proposed model is effective for predicting TBM cutter wear under complex geological conditions.

Evaluation and optimization of drilling efficiency while drilling based on improved rock-breaking specific energy model of bit

Rock-breaking specific energy model of bit is the key foundation of evaluation and optimization of downhole drilling condition, while some necessary parameters for the existing models is difficult to measure directly while drilling. Aiming to improve the accessibility and accuracy of the model, this study proposes and illustrates an improved method by combining field tests and logging while drilling. The improved model was used to distinguish the inefficient drilling conditions and then to optimize drilling parameters. The results of application cases show that, the necessary parameters for the improved model could be obtained in time and the improved model helps to distinguish the reasons of stick–slip, whirling, load transfer difficulties in different horizontal wells. Based on the proposed model, the drilling parameters were evaluated and optimized, and then the rate of penetration get improved by 90.5% with higher energy efficiency. The proposed model and method could facilitate to realize automated and intelligent drilling.

Engineering Stimuli-Responsive Materials for Precision Medicine.

Over the past decade, precision medicine has garnered increasing attention, making significant strides in discovering new therapeutic drugs and mechanisms, resulting in notable achievements in symptom alleviation, pain reduction, and extended survival rates. However, the limited target specificity of primary drugs and inter-individual differences have often necessitated high-dosage strategies, leading to challenges such as restricted deep tissue penetration rates and systemic side effects. Material science advancements present a promising avenue for these issues. By leveraging the distinct internal features of diseased regions and the application of specific external stimuli, responsive materials can be tailored to achieve targeted delivery, controllable release, and specific biochemical reactions. This review aims to highlight the latest advancements in stimuli-responsive materials and their potential in precision medicine. Initially, we introduce disease-related internal stimuli and capable external stimuli, elucidating the reaction principles of responsive functional groups. Subsequently, we provide a detailed analysis of representative pre-clinical achievements of stimuli responsive materials across various clinical applications, including enhancements in the treatment of cancers, injury diseases, inflammatory diseases, infection diseases, and high-throughput microfluidic biosensors. Finally, we discuss some clinical challenges, such as off-target effects, long-term impacts of nano-materials, potential ethical concerns, and offer insights into future perspectives of stimuli-responsive materials.

Toxicokinetic model of the pyrethroid pesticide lambda-cyhalothrin, main exposure route and dose reconstruction predictions in agricultural workers.

A toxicokinetic model of the pyrethroid insecticide lambda-cyhalothrin (LCT) was developed to relate absorbed doses to urinary cis-3-(2-chloro-3,3,3-trifluoroprop-1-en-1-yl)-2,2-dimethylcyclopropanecarboxylic acid (CFMP) metabolite levels used as a biomarker of exposure. The model then served to reconstruct absorbed doses in agricultural workers and their probability of exceeding the EFSA Acceptable occupational Exposure Level (AOEL). The toxicokinetic model was able to reproduce the temporal profiles of CFMP in the urine of operators spraying pesticides using the optimized model parameters (adjusted to human volunteer data). Modeling also showed that simulation of an inadvertent oral exposure mainly was the exposure scenario giving the best fit to CFMP urinary time-course data in applicators. With the dermal model parameters optimized from data in volunteers, simulation of a dermal exposure in applicators did not allow to reproduce the observed peak excretions and urinary metabolite levels; extremely high applied dermal doses would be required but still simulated dermal penetration rate would remain too slow. Simulation of an inhalation exposure allowed to reproduce the observed time-courses, but with unrealistic air concentrations. For applicators with the highest urinary concentrations, there was a probability of exceeding the AOEL at some points during the biomonitoring period [>50% probability of exceeding for 27% of 24-h samples]; for non-applicator workers the probability of exceeding the AOEL value was very low [corresponding value of 5%]. Furthermore, the median [95% CI] estimates of 10 000 Monte Carlo simulations led to a biological reference value corresponding to the AOEL of 116 [113-119] ng/kg bw/d and 7.5 [7.3-7.7] μg/L. Overall, 7% of applicators and 1% of workers performing weeding and strawberry picking had a probability of exceeding this biological reference value. As a next step, it would be interesting to apply these methods to multiple exposure to various contaminants.

The Coupling Coordination Relationship and Driving Factors of the Digital Economy and High-Quality Development of Rural Tourism: Insights from Chinese Experience Data

Globally, rural tourism development faces challenges such as inadequate infrastructure, insufficient marketing resources, and unreliable service quality, all of which limit its potential. However, digital technology offers unprecedented opportunities to address these barriers. China’s experience in integrating digital technology into rural tourism provides a valuable case study for understanding the digitalization of rural tourism. This study constructs an index system to assess the coupling coordination relationship between the digital economy and the high-quality development of rural tourism (HQDRT). By employing methods such as the entropy method, coupling coordination degree model, obstacle factor model, and geographic detector, the study examines the evolution of this coupling coordination relationship and its driving mechanisms across 31 provinces (including regions and municipalities) in China from 2012 to 2021. The findings reveal that (1) The development of the digital economy generally lags behind that of the rural tourism, but the coupling coordination relationship between the two is steadily improving. (2) The level of coupling coordination increases from west to east, with spatial distribution patterns evolving from ‘antagonism’ to ‘adaptation’ and then to ‘coordination’ as they move eastward. Most provinces belong to the ‘adaptation’ type. (3) From a nationwide perspective, the primary obstacles impeding the development of the digital economy include an insufficient internet penetration rate, which consequently leads to underdeveloped internet finance development and telecommunications industry development. The major barriers to the HQDRT stem from an inadequate number of tourists and a lack of physical infrastructure. (4) Population density, consumer spending, and R&amp;D are significant drivers of the coupling coordination relationship, with the interaction between urbanization rates and other factors generally weakening the degree of coupling.

Analytical Solutions for Composite Foundations Reinforced by Partially Penetrated Stone Columns and Vertical Drains

ABSTRACTWhen stone columns or vertical drains are applied to improve soils, it is common to face situations where the soft soil layer is too thick to be penetrated completely. Although consolidation theories for soils with partially penetrated vertical drains or stone columns are comprehensive, consolidation theories for impenetrable composite foundations containing both two types of drainage bodies have been few reported in the existing literature. Equations governing the consolidation of the reinforced zone and unreinforced zone are established, respectively. Analytical solutions for consolidation of such composite foundations are obtained under permeable top with impermeable bottom (PTIB) and permeable top with permeable bottom (PTPB), respectively. The correctness of proposed solutions is verified by comparing them with existing solutions and finite element analyses. Then, extensive calculations are performed to analyze the consolidation behaviors at different penetration rates, including the total average consolidation degree defined by strain or stress and the distribution of the average excess pore water pressure (EPWP) along the depth. The results show that the total average consolidation rate increases as the penetration rate increases; for some composite foundations with a low penetration rate, the consolidation of the unreinforced zone cannot be ignored. Finally, according to the geological parameters provided by an actual project, the obtained solution is used to calculate the settlement, and the results obtained by the proposed solution are in reasonable agreement with the measured data.

How Connected Cars Could Capture Cloud Dynamics for Solar Forecasting—Evidence from Two Simulation Scenarios

The rapidly increasing share of fluctuating electricity from photovoltaics calls for accurate approaches to estimate cloud motion, the primary source for the varying power supply. While local sensor networks are prominent in targeting forecast horizons too short for image‐based methods, they have minimal spatial coverage. This work presents the first step towards expanding those approaches to spatially scalable sensor networks: With the motivation of using automotive light sensors as a sensor network, two excerpts from a microscopic traffic simulation serve as simulative sensor networks. A fractal‐based cloud shadow pattern passes the sensor network areas with defined velocities and directions, which shall be estimated using the cumulative mean absolute error method. The evaluation results indicate that the more extensive observation areas compensate for the dynamics in the sensor network when compared to a reference work with a static sensor grid. Furthermore, this work shows how the estimates deteriorate with lower vehicle penetration rates (PR) and longer building shadows due to a lower solar elevation angle. At a penetration rate of 40%, the root mean square errors for both sensor networks are still below 5 ms−1. In conclusion, the spatiotemporal characteristics of a vehicle network offer a potential for estimating cloud movements.

Based on improved crayfish optimization algorithm cooperative optimal scheduling of multi-microgrid system

In order to solve the influence of the complex interaction relationships among subjects on the system solution accuracy and speed of the Multi-Microgrid system under the high penetration rate of new energy. Firstly, the paper establishes the bi-level optimal scheduling Stackelberg game model based on shared energy storage, considering the inter-subject interaction in MMG. Subsequently, based on the four improvement methods of Chaotic Map, Quantum Behavior, Gaussian Distribution, and Nonlinear Control Strategy, the Chaotic Gaussian Quantum Crayfish Optimization Algorithm is proposed to solve the optimization scheduling model. The improved algorithm exhibits superior initial solutions and enhanced search capability. In comparison to the original algorithm, the relative errors of the CGQCOA optimization outcomes are 98%, 20.96%, 98.74% and 16.55%, respectively, enhancing the model-solving accuracy and the speed of convergence to the optimal solution. Finally, the simulation demonstrates that the revenue of Microgrid 1, Microgrid 2, and Microgrid 3 have increased by 0.73%, 1.17%, and 1.04%, respectively. Concurrently, the penalty cost of pollutant emission has decreased by 5.9%, 11.5%, and 12.68%, respectively. Furthermore, the revenue of the shared storage have increased by 1.91%. These findings validate the efficacy of the methodology proposed in enhancing the revenue of the various subjects and reducing pollutant gas emission.

Success Rate of Drilling and Knuckle Wire Crossing Technique in Chronic total Occlusion of the Femoro-Popliteal Artery and Predicting Factors Related to it in Real-World Data

Objective: Crossing chronic total occlusion (CTO) in femoro-popliteal (FP) arteries is challenging. Hence, this study aimed to describe real-world data on the technical success rate of crossing FP CTO lesions, and analyze predicting factors for crossing failure. Material and Methods: This retrospective study, conducted at a single center, involved patients with peripheral arterial disease (PAD) and FP CTOs, categorized as Rutherford categories 3-6, having undergone endovascular recanalization from 2017 to 2022. Baseline characteristics and crossing success rates were described using frequencies, percentages, means, medians, standard deviations, and interquartile ranges. Factors associated with crossing failure were analyzed through logistic regression analysis, with statistical significance set at a p-value&lt;0.05.Results: Among the 181 patients analyzed, the majority exhibited minor tissue loss (63%) and severe disease (TASC II category D, GLASS grade 4). The predominant CTO crossing approach was based on plaque cap morphology (CTOP) classification type B (66.9%), with severe CTO calcification (PACSS type 4) in 23.8%. Median occlusion length was 16.1 cm; 85.6% successfully crossed the CTO and antegrade success was 57.5%. Factors associated with crossing failure included: CTOP types C or D, CTO at the P3-popliteal artery, severe CTO calcification, and dyslipidemia. Retrograde recanalization showed higher success rates in CTOP type D and longer occlusions.Conclusion: This study highlights the safety and effectiveness of the catheter-wire crossing technique for FP CTOs. CTOP classification, CTO location, severe calcification, and dyslipidemia predicted crossing failure. The retrograde approach works well in CTOP type D and longer CTOs, providing insights for improving success in clinical practice.

Optimal Flexibility Dispatching of Multi-Pumped Hydro Storage Stations Considering the Uncertainty of Renewable Energy

With the continuous increase in the penetration rate of renewable energy, the randomness and flexibility demand in the power system continues to increase. The main grid side of the power system vigorously develops pumped hydro storage (PHS) resources. However, the current PHS station scheduling method of a fixed time period and fixed power has lost a certain flexibility supply. In this paper, an optimal dispatching model of multi-pumped hydro storage stations is proposed to supply flexibility for different regions of the state grid in east China. Firstly, the credible predictable power (CPP) of renewable energy is calculated and the definition of flexibility demand of a power system is given. The calculation model for flexibility demand is established. Secondly, considering the regional allocation constraint in the state grid in east China, a non-centralized model of multi-PHS within the dispatch scope is established. In the model, the constraints of storage capacity of different hydropower conversion coefficients of each PHS station is considered. The flexibility supply model of PHS stations to each region of the state grid in east China is established to realize reasonable flexibility allocation. Then, by combining the PHS station models and the flexibility demand calculation model, the optimal dispatching model for the flexibility supply of multi-PHS stations is established. Finally, based on the network dispatching example, the effectiveness and superiority of the proposed strategy are verified by a case study.