A new prediction model for hydrostatic pressure reduction of anti-gas channeling cement slurry based on large-scale physical modeling experiments

2019 ◽  
Vol 172 ◽  
pp. 259-268
Author(s):  
Yijin Zeng ◽  
Peiqing Lu ◽  
Shiming Zhou ◽  
Laiyu Sang ◽  
Rengguang Liu ◽  
...  
Keyword(s):  
Hydrostatic Pressure ◽  
Prediction Model ◽  
Physical Modeling ◽  
Large Scale ◽  
Pressure Reduction ◽  
Cement Slurry ◽  
Gas Channeling

Bioactivity Prediction Based on Matched Molecular Pair and Matched Molecular Series Methods

2020 ◽  
Vol 26 (33) ◽  
pp. 4195-4205
Author(s):  
Xiaoyu Ding ◽  
Chen Cui ◽  
Dingyan Wang ◽  
Jihui Zhao ◽  
Mingyue Zheng ◽  
...  
Keyword(s):  
Prediction Model ◽  
Large Scale ◽  
Prediction Models ◽  
Predictive Accuracy ◽  
Lead Optimization ◽  
Consensus Method ◽  
Molecular Pair ◽  
Bioactivity Prediction ◽  
Compound Synthesis ◽  
Consensus Modeling

Background: Enhancing a compound’s biological activity is the central task for lead optimization in small molecules drug discovery. However, it is laborious to perform many iterative rounds of compound synthesis and bioactivity tests. To address the issue, it is highly demanding to develop high quality in silico bioactivity prediction approaches, to prioritize such more active compound derivatives and reduce the trial-and-error process. Methods: Two kinds of bioactivity prediction models based on a large-scale structure-activity relationship (SAR) database were constructed. The first one is based on the similarity of substituents and realized by matched molecular pair analysis, including SA, SA_BR, SR, and SR_BR. The second one is based on SAR transferability and realized by matched molecular series analysis, including Single MMS pair, Full MMS series, and Multi single MMS pairs. Moreover, we also defined the application domain of models by using the distance-based threshold. Results: Among seven individual models, Multi single MMS pairs bioactivity prediction model showed the best performance (R2 = 0.828, MAE = 0.406, RMSE = 0.591), and the baseline model (SA) produced the most lower prediction accuracy (R2 = 0.798, MAE = 0.446, RMSE = 0.637). The predictive accuracy could further be improved by consensus modeling (R2 = 0.842, MAE = 0.397 and RMSE = 0.563). Conclusion: An accurate prediction model for bioactivity was built with a consensus method, which was superior to all individual models. Our model should be a valuable tool for lead optimization.

Applications of Nanomaterials in Wellbore Fluids in Oil and Gas Fields

2021 ◽  
Vol 881 ◽  
pp. 33-37
Author(s):  
Wei Na Di
Keyword(s):  
Oil And Gas ◽  
Drill String ◽  
Wellbore Stability ◽  
Petroleum Engineering ◽  
Cement Stone ◽  
Drilling Fluids ◽  
Cement Slurry ◽  
Oil And Gas Fields ◽  
Gas Channeling ◽  
Gas Fields

The application of nanomaterials in oil and gas fields development has solved many problems and pushed forward the development of petroleum engineering technology. Nanomaterials have also been used in wellbore fluids. Nanomaterials with special properties can play an important role in improving the strength and flexibility of mud cake, reducing friction between the drill string and wellbore and maintaining wellbore stability. Adding nanomaterials into the cement slurry can eliminate gas channeling through excellent zonal isolation and improve the cementing strength of cement stone, thereby facilitating the protection and discovery of reservoirs and enhancing the oil and gas recovery. This paper tracks the application progress of nanomaterials in wellbore fluids in oil and gas fields in recent years, including drilling fluids, cement slurries. Through the tracking and analysis of this paper, it is concluded that the applications of nanomaterials in wellbore fluids in oil and gas fields show a huge potential and can improve the performance of wellbore fluids.

scholarly journals Machine Learning-Based Prediction Model for Papillary Thyroid Carcinoma Recurrence

2020 ◽  
Author(s):  
Young Min Park ◽  
Byung-Joo Lee
Keyword(s):  
Machine Learning ◽  
Prediction Model ◽  
Tumor Size ◽  
Large Scale ◽  
Prediction Models ◽  
Prognostic Significance ◽  
Disease Recurrence ◽  
Machine Learning Techniques ◽  
Papillary Thyroid ◽  
Recurrence Prediction

Abstract Background: This study analyzed the prognostic significance of nodal factors, including the number of metastatic LNs and LNR, in patients with PTC, and attempted to construct a disease recurrence prediction model using machine learning techniques.Methods: We retrospectively analyzed clinico-pathologic data from 1040 patients diagnosed with papillary thyroid cancer between 2003 and 2009. Results: We analyzed clinico-pathologic factors related to recurrence through logistic regression analysis. Among the factors that we included, only sex and tumor size were significantly correlated with disease recurrence. Parameters such as age, sex, tumor size, tumor multiplicity, ETE, ENE, pT, pN, ipsilateral central LN metastasis, contralateral central LNs metastasis, number of metastatic LNs, and LNR were input for construction of a machine learning prediction model. The performance of five machine learning models related to recurrence prediction was compared based on accuracy. The Decision Tree model showed the best accuracy at 95%, and the lightGBM and stacking model together showed 93% accuracy. Conclusions: We confirmed that all machine learning prediction models showed an accuracy of 90% or more for predicting disease recurrence in PTC. Large-scale multicenter clinical studies should be performed to improve the performance of our prediction models and verify their clinical effectiveness.

Physical Modeling for Selective Laser Sintering Process

2017 ◽  
Vol 17 (2) ◽  
Author(s):  
Arash Gobal ◽  
Bahram Ravani
Keyword(s):  
Physical Modeling ◽  
Large Scale ◽  
Selective Laser Sintering ◽  
Powdered Material ◽  
Laser Sintering ◽  
Industrial Applications ◽  
Transient Temperature ◽  
Sintering Process ◽  
Powder Bed ◽  
Selective Heating

The process of selective laser sintering (SLS) involves selective heating and fusion of powdered material using a moving laser beam. Because of its complicated manufacturing process, physical modeling of the transformation from powder to final product in the SLS process is currently a challenge. Existing simulations of transient temperatures during this process are performed either using finite-element (FE) or discrete-element (DE) methods which are either inaccurate in representing the heat-affected zone (HAZ) or computationally expensive to be practical in large-scale industrial applications. In this work, a new computational model for physical modeling of the transient temperature of the powder bed during the SLS process is developed that combines the FE and the DE methods and accounts for the dynamic changes of particle contact areas in the HAZ. The results show significant improvements in computational efficiency over traditional DE simulations while maintaining the same level of accuracy.

Large-scale physical modeling of broken solitary waves impacting elevated coastal structures

2022 ◽  
pp. 1-21
Author(s):  
Clemens Krautwald ◽  
Hajo Von Häfen ◽  
Peter Niebuhr ◽  
Katrin Vögele ◽  
David Schürenkamp ◽  
...  
Keyword(s):  
Solitary Waves ◽  
Physical Modeling ◽  
Large Scale ◽  
Coastal Structures

scholarly journals Development of Two-Dimensional Non-Hydrostatic Wave Model Based on Central-Upwind Scheme

2020 ◽  
Vol 8 (7) ◽  
pp. 505
Author(s):  
Gangfeng Wu ◽  
Ying-Tien Lin ◽  
Ping Dong ◽  
Kefeng Zhang
Keyword(s):  
Hydrostatic Pressure ◽  
Large Scale ◽  
Wave Model ◽  
Water Model ◽  
Two Dimensional ◽  
Shallow Water Model ◽  
Practical Engineering ◽  
Hydrostatic Model ◽  
Good Agreement ◽  
Biconjugate Gradient

In this study, a two-dimensional depth-integrated non-hydrostatic wave model is developed. The model solves the governing equations with hydrostatic and non-hydrostatic pressure separately. The velocities under hydrostatic pressure conditions are firstly obtained and then modified using the biconjugate gradient stabilized method. The hydrostatic front approximation (HFA) method is used to deal with the wave breaking issue, and after the wave breaks, the non-hydrostatic model is transformed into the hydrostatic shallow water model, where the non-hydrostatic pressure and vertical velocity are set to zero. Several analytical solutions and laboratory experiments are used to verify the accuracy and robustness of the developed model. In general, the numerical simulations are in good agreement with the theoretical or experimental results, which indicates that the model is able to simulate large-scale wave motions in practical engineering applications.

Arch Dam Deformation Prediction Model Based on PSO-SVM

2013 ◽  
Vol 351-352 ◽  
pp. 1306-1311 ◽  
Author(s):  
Jing Yang Liu ◽  
He Zhi Liu
Keyword(s):  
Prediction Model ◽  
Large Scale ◽  
Small Sample ◽  
Arch Dam ◽  
Support Vector ◽  
Model Parameters ◽  
Deformation Prediction ◽  
Dam Foundation ◽  
Model Based ◽  
Dam Deformation

Arch dam has gradually evolved as one of dam type as main large-scale hydraulic project, dam deformation prediction is an important part of dam safety monitoring, and it is difficult to forecast because of the complicated nonlinear characteristics of the monitoring data. Support Vector Machine (SVM) could solve the small sample, nonlinear high dimension problem due to the excellent generalization ability, and hence it has been widely used in the forecast of arch dam deformation. However, the forecast results considerably depend on the choice of SVM model parameters. In this paper, Particle Swarm Optimization (PSO), which has the characteristic of fast global optimization, was applied to optimize the parameters in SVM, and then the dam deformation prediction model based on PSO-SVM could be established. The model is applied to a certain arch dam foundation prediction. The accuracy of this employed approach was examined by comparing it with multiple regression method. In a word, the experimental results indicate that the proposed method based on PSO-SVM can be used in arch dam deformation prediction.

scholarly journals Assessment of embankment dams breaching using large scale physical modeling and statistical methods

Water Science ◽  
2018 ◽  
Vol 32 (2) ◽  
pp. 362-379 ◽  
Author(s):  
Muhammad Ashraf ◽  
Ahmed Hussein Soliman ◽  
Entesar El-Ghorab ◽  
Alaa El Zawahry
Keyword(s):  
Statistical Methods ◽  
Physical Modeling ◽  
Large Scale ◽  
Embankment Dams
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