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2022 ◽  
Vol 14 (1) ◽  
pp. 168781402110729
Author(s):  
Peng Cancan ◽  
Zhang Xiaodong ◽  
Gao Zhiguang ◽  
Wu Ju ◽  
Gong Yan

Multiphase pumps play an important role in the exploitation of natural gas hydrate. Compared with ordinary pumps, they can handle fluids with higher gas volume fraction (GVF). Therefore, it is important to improve the performance of the pump under high GVF. A model pump is designed based on the design theory of axial flow pump and centrifugal pump inducer. The hydraulic performance of the model pump is verified by numerical simulation and experiment. The Sparse Grid method is applied to the design of experiment (DOE), and three different adaptive refined response surface methods (RSM) are applied to the build the approximate model. Refinement points and verification points are used to improve and verify the precision of the response surface, respectively. The model with high precision and high computational efficiency is obtained through comparison and analysis. The multi-objective optimization of the optimal response surface model is carried out by MOGA (Multi-Objective Genetic Algorithm) method. The pressure increment of the optimized model is increased by 38 kPa. The efficiency is significantly improved under large mass flow conditions. The hydraulic performance of the optimized model is compared with that of the basic model. And the reasons that affect the performance of the multiphase pump are analyzed.


2021 ◽  
pp. 001112872110647
Author(s):  
Wanda E. Leal ◽  
Alex R. Piquero ◽  
Justin Kurland ◽  
Nicole Leeper Piquero ◽  
Elizabeth L. Gloyd

The current study investigates the effects of coronavirus restrictions on family violence in the seventh largest city in the country, San Antonio, Texas. Two streams of data were used to evaluate the potential change between what occurred during the lockdown period versus what would have been expected, including the COVID-19 Government Response Stringency Index and police calls for service from the San Antonio Police Department. The methodological approach used takes advantage of feature engineering, various machine learning time series forecasting techniques commonly leveraged in financial technical analysis, as well as cross-validation for optimized model selection. These techniques have not been considered in previous domestic or family violence-related research. During the lockdown period in San Antonio, we observed a larger than expected increase in calls to police for family violence incidents. Specifically, an increase of over fourteen percent of police calls for family incidents was observed. The findings of the current study suggest that social service and social welfare agencies consider and plan for how future pandemics or other major disasters will affect the incidence of family violence and take appropriate steps now to bolster resources and scale up for the future.


2021 ◽  
Author(s):  
Asghar Ashari Moghaddam ◽  
Sorayya Nouri sangarab ◽  
Ali Kadkhodaie Ilkhchi

Abstract The vulnerability of groundwater, as the primary source of water for human survival, should be assessed for the purpose of pollution management. The Ajabshir plain, one of the major agricultural areas in the northwest of Iran, is always prone to pollution. Therefore, to prevent the increase in pollution, it is necessary to determine the polluting factors and areas prone to groundwater pollution. In this study, by modifying the DRASTIC method using the land-use layer, called DRASTICL, vulnerable areas and pollution index were mapped. To ensure dealing with the uncertainty of the parameters, the DRASTICL model was optimized utilizing the Sugeno-type fuzzy inference system. The models were validated based on nitrate pollution. The correlation of DRASTICL and its optimized model with the nitrate pollution are 0.31 and 0.80, respectively. The results of this study show that integrating the DRASTIC model and fuzzy knowledge is an instrumental way for assessment of vulnerability potential.


2021 ◽  
Vol 11 (23) ◽  
pp. 11149
Author(s):  
Jinlai Qi ◽  
Youping Gong ◽  
Honghao Chen ◽  
Junling He ◽  
Zizhou Qiao ◽  
...  

To solve the mismatch between the comprehensive mechanical properties of the spinal fusion cage and body, a fusion cage inner hole design method based on controllable TPMS-P to characterize the inner hole structure is proposed to solve the related problems. Firstly, the method of TPMS-P parameterization was used to construct the bionic porous structure model, which was designed as the linear gradual internal porous structure model. Then, we optimized the topology of the obtained porous structure implants to achieve precise control of the overall comprehensive mechanical properties of the fusion cage structure and obtain an optimized model that matched the mechanical properties of the fusion cage. To verify whether the method met the requirements, its simulation model was established. The porous structure was fabricated by selective laser processing, and its properties were tested and analyzed. The results show that its yield strength is 79.83 MPa, which match well with spinal bone tissue.


2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Danyang Dong ◽  
Yi Yu ◽  
Huahua Zhao ◽  
Pengzi Chu ◽  
Hui Lin

Auxiliary stopping area (ASA) is the key guarantee for high-speed maglev train operation safety. Aiming at the layout of the ASA along the track, this article constructs an optimized model of ASA layout and proposes a solving algorithm based on the single target genetic algorithm by analyzing the influence factors of ASA layout. The result of the numerical experiment shows that the proposed method could fulfill the requirements of the maglev train operation safety and efficiency and optimize the cost of the ASA layout while taking the complex line situation, train tracking operation, and bidirectional operation on a single line into account.


2021 ◽  
Vol 11 (22) ◽  
pp. 11066
Author(s):  
Jun-Hwan Kwon ◽  
Jae-Kyung Kim ◽  
Euy-Sik Jeon

The aim of this paper is to present the optimal design process and an optimized model for a discontinuous armature arrangement permanent magnet linear synchronous motor (PMLSM). The stator tooth shapes are optimized to reduce detent force. When the shape of the stator is changed to reduce the detent force, the saturation magnetic flux density and the back electromotive force characteristics change. Multi-objective optimization is used to search for the local lowest point that can improve the detent force, saturation magnetic flux density, and back EMF characteristics. To reduce the detent force generated at the outlet edge, a trapezoidal auxiliary tooth was installed and the performance was analyzed. The experiment’s response surface methodology is used as an optimization method and all the experimental samples are obtained from finite-element analysis. The validity of this method is verified by comparing the optimized FEA model to the initial FEA model.


2021 ◽  
Author(s):  
Milot Mirdita ◽  
Konstantin Schütze ◽  
Yoshitaka Moriwaki ◽  
Lim Heo ◽  
Sergey Ovchinnikov ◽  
...  

Abstract ColabFold offers accelerated protein structure and complex predictions by combining the fast homology search of MMseqs2 with AlphaFold2 or RoseTTAFold. ColabFold's 20-30x faster search and optimized model use allows predicting thousands of proteins per day on a server with one GPU. Coupled with Google Colaboratory, ColabFold becomes a free and accessible platform for protein folding. ColabFold is open-source software available at github.com/sokrypton/ColabFold. Its novel environmental databases are available at colabfold.mmseqs.com.


2021 ◽  
Vol 22 (22) ◽  
pp. 12261
Author(s):  
Mariia R. Mollaeva ◽  
Nikita Yabbarov ◽  
Maria Sokol ◽  
Margarita Chirkina ◽  
Murad D. Mollaev ◽  
...  

The selection of technological parameters for nanoparticle formulation represents a complicated development phase. Therefore, the statistical analysis based on Box–Behnken methodology is widely used to optimize technological processes, including poly(lactic-co-glycolic acid) nanoparticle formulation. In this study, we applied a two-level three-factor design to optimize the preparation of nanoparticles loaded with cobalt (CoTPP), manganese (MnClTPP), and nickel (NiTPP) metalloporphyrins (MeP). The resulting nanoparticles were examined by dynamic light scattering, X-ray diffraction, Fourier transform infrared spectroscopy, MTT test, and hemolytic activity assay. The optimized model of nanoparticle formulation was validated, and the obtained nanoparticles possessed a spherical shape and physicochemical characteristics enabling them to deliver MeP in cancer cells. In vitro hemolysis assay revealed high safety of the formulated MeP-loaded nanoparticles. The MeP release demonstrated a biphasic profile and release mechanism via Fick diffusion, according to release exponent values. Formulated MeP-loaded nanoparticles revealed significant antitumor activity and ability to generate reactive oxygen species. MnClTPP- and CoTPP-nanoparticles specifically accumulated in tissues, preventing wide tissue distribution caused by long-term circulation of the hydrophobic drug. Our results suggest that MnClTPP- and CoTPP-nanoparticles represent the greatest potential for utilization in in anticancer therapy due to their effectiveness and safety.


2021 ◽  
Vol 13 (21) ◽  
pp. 12265
Author(s):  
Pablo Garrido Martínez-Llop ◽  
Juan de Dios Sanz Bobi ◽  
Álvaro Solano Jiménez ◽  
Jorge Gutiérrez Sánchez

Recently, passenger comfort and user experience are becoming increasingly relevant for the railway operators and, therefore, for railway manufacturers as well. The main reason for this to happen is that comfort is a clear differential value considered by passengers as final customers. Passengers’ comfort is directly related to the accelerations received through the car-body of the train. For this reason, suspension and damping components must be maintained in perfect condition, assuring high levels of comfort quality. An early detection of any potential failure in these systems derives in a better maintenance inspections’ planification and in a more sustainable approach to the whole train maintenance strategy. In this paper, an optimized model based on neural networks is trained in order to predict lateral car-body accelerations. Comparing these predictions to the values measured on the train, a normal characterisation of the lateral dynamic behaviour can be determined. Any deviation from this normal characterisation will imply a comfort loss or a potential degradation of the suspension and damping components. This model has been trained with a dataset from a specific train unit, containing variables recorded every second during the year 2017, including lateral and vertical car-body accelerations, among others. A minimum average error of 0.034 m/s2 is obtained in the prediction of lateral car-body accelerations. This means that the average error is approximately 2.27% of the typical maximum estimated values for accelerations in vehicle body reflected in the EN14363 for the passenger coaches (1.5 m/s2). Thus, a successful model is achieved. In addition, the model is evaluated based on a real situation in which a passenger noticed a lack of comfort, achieving excellent results in the detection of atypical accelerations. Therefore, as it is possible to measure acceleration deviations from the standard behaviour causing lack of comfort in passengers, an alert can be sent to the operator or the maintainer for a non-programmed intervention at depot (predictive maintenance) or on board (prescriptive maintenance). As a result, a condition-based maintenance (CBM) methodology is proposed to avoid comfort degradation that could end in passenger complaints or speed limitation due to safety reasons for excessive acceleration. This methodology highlights a sustainable maintenance concept and an energy efficiency strategy.


Energies ◽  
2021 ◽  
Vol 14 (21) ◽  
pp. 7285
Author(s):  
Ammar Bany Ata ◽  
Peter Maximilian Seufert ◽  
Christian Heinze ◽  
Falah Alobaid ◽  
Bernd Epple

Efficient and flexible operation is essential for competitiveness in the energy market. However, the CO2 emissions of conventional power plants have become an increasingly significant environmental dilemma. In this study, the optimization of a steam power process of an IGCC was carried out, which improved the overall performance of the plant. CCPP with a subcritical HRSG was modelled using EBSILON Professional. The numerical results of the model were validated by measurements for three different load cases (100, 80, and 60%). The results are in agreement with the measured data, with deviations of less than 5% for each case. Based on the model validation, the model was modified for the use of syngas as feed and the integration of heat into an IGCC process. The integration was optimized with respect to the performance of the CCPP by varying the extraction points, adjusting the steam parameters of the extractions and modifying the steam cycle. For the 100% load case, a steam turbine power achieved increase of +34.2%. Finally, the optimized model was subjected to a sensitivity analysis to investigate the effects of varying the extraction mass flows on the output.


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