Groups Of Experimental Data Research Articles

Simulation and parameter determination of the net sorption of phenanthrene by sediment particles

The distribution of polycyclic aromatic hydrocarbons (PAHs) in the ocean is affected by the sorption-desorption process of sediment particles. This process is determined by the concentration of PAHs in seawater, water temperature, and organic matter content of sediment particles. Quantitative relationships between the net sorption rates (=the difference of sorption and desorption rates) and these factors have not been established yet and used in PAH transport models. In this study, phenanthrene was chosen as the representative of PAHs. Three groups of experimental data were collected to address the dependence of the net sorption processes on the initial concentration, water temperature, and organic carbon content representing organic matter content. One-site and two-compartment mass-transfer models were tested to represent the experimental data using various parameters. The results showed that the two-compartment mass-transfer model performed better than the one-site mass-transfer model. The parameters of the two-compartment mass-transfer model include the sorption rate coefficients kafand kas (L g−1 min−1), and the desorption rate coefficients kdf and kds (min−1). The parameters at different temperatures and organic carbon contents were obtained by numerical simulations. Linear relationships were obtained between the parameters and water temperature, as well as organic carbon content. kaf, kas and kdf decreased linearly, while kds increased linearly with temperature. kaf, kas and kdf increased linearly, while kds decreased linearly with organic carbon content. The r2 values between the simulation results based on the relationships and the experimental results reached 0.96–0.99, which supports the application of the model to simulate sorption-desorption processes at different water temperatures and organic carbon contents in a realistic ocean.

A new elasto-visco-plastic damage model and numerical simulation method used for time-dependent behavior prediction of deep tunnel

Accurately predicting the time-dependent behavior of surrounding rock is crucial for supporting design, revealing the mechanism of lining cracking and understanding long-term stability in deep-buried tunnels engineering. Based on the component combination model method and continuous damage mechanics theory, a New Elasto-Visco-Plastic Damage (NEVPD) model was proposed considering the characteristics of viscoelastic-plastic and the damage evolution of rocks. Three groups of experimental data were used for validating the model, and the results indicate that the model can effectively describe the time-dependent deformation characteristics of rocks at different stress levels. The dynamic link computer application program of the NEVPD model was procured through the secondary development interface of the FLAC3D software, and its validity was ascertained through test simulations. By investigating the influence of stress levels on creep parameters, a creep simulation approach considering the impact of rock mass stress state on the creep parameters was proposed for used in the long-term stability analysis of underground rock engineering. Finally, the time-dependent behavior of deep tunnel was predicted using two simulation approaches (the traditional method and the proposed method) based on the Burger-Creep Viscoplastic (CVISC) model built-in FLAC3D and the NEVPD model. The findings suggest that the proposed tunnel creep simulation approach provides a more precise reflection of the local deformation features of the surrounding rock compared to traditional methods. The simulation outcomes based on the NEVPD model exhibit the highest agreement with the field monitoring data.

A study on the influence of FIFA 11+ practice on knee proprioception and dynamic balance ability of young campus football players

To study the effect of FIFA 11 + warm-up on knee proprioception and dynamic balance of adolescent football players. 30 young school football players were randomly divided into two groups: FIFA 11 + training group and routine warm-up training group. The dominant leg of all athletes is the right leg. FIFA 11 + training was arranged once a day in the pre training warm-up stage of FIFA 11 + group， while the control group was given regular warm-up exercise with the team. Two groups of experimental data were collected and recorded. The closer the feeling (active and passive) of the knee joint was to 135 degrees， the better the result was. In the dynamic balance test of the knee joint (YBT)， the scores of the non supporting leg in front were recorded as a， the scores of the non supporting leg in the rear inner side were recorded as PM， the scores of the non supporting leg in the rear outer side were recorded as PL， and the comprehensive scores of YBT were recorded as ybt-cs， The higher the score of analysis and comparison results， the better the balance and stability function. The difference of the above recorded data was analyzed by independent sample t-test， and the significance level a was 0.05. After FIFA After 11 + training， there were significant differences in the comparison values of proprioception (active and passive) between the knee joint groups (P (0.05) compared with the control group， the difference was significant. After FIFA 11 + warm-up exercise， it can significantly improve the knee proprioception and dynamic balance ability of young school football players. Therefore， coaches should be encouraged to use FIFA 11 + warm-up activities in the future football training match warm-up activities.

Uncertainty quantification of SSG/LRR-ω turbulence model closure coefficients

The accurate simulation of complex turbulent flow has remained a challenging issue in computational fluid dynamics and plays a crucial role in engineering practice. Due to computational limitations, the Reynolds Averaged Navier-Stokes (RANS) simulation method continues to be the preferred choice for engineers to solve practical problems. Nonetheless, there are concerns regarding the applicability of RANS models to complex flows such as those with large-scale separation or jet interaction, as recommended coefficients may not be suitable. This work aims to perform a specified analysis of parameters' uncertainty in the Speziale-Sarkar-Gatsk/Launder-Reece-Rodi (SSG/LRR)-ω turbulence model. This work employs the non-intrusive polynomial chaos (NIPC) method to establish a surrogate model between parameters and computational results for uncertainty quantification. Sensitivity analysis is conducted via the Sobol index to identify key parameters critical for different flow structures. Then, Bayesian inference is applied for calibrating parameters by leveraging calculation results and two groups of experimental data, respectively. The results show that the calibrated values for the key parameters could significantly improve prediction accuracy. Subsequently, the work also delves into the inadequacies of SSG/LRR-ω models when applied to separated flows, revealing an underestimation of Reynolds stress in the separation zone as a primary source of poor predictions. Furthermore, it is considered that using a set of parameter combinations with a larger value of C3ω after the separation of the flow will obtain better calculation results in the separation zone.

Inference on autoregulation in gene expression with variance-to-mean ratio

Some genes can promote or repress their own expressions, which is called autoregulation. Although gene regulation is a central topic in biology, autoregulation is much less studied. In general, it is extremely difficult to determine the existence of autoregulation with direct biochemical approaches. Nevertheless, some papers have observed that certain types of autoregulations are linked to noise levels in gene expression. We generalize these results by two propositions on discrete-state continuous-time Markov chains. These two propositions form a simple but robust method to infer the existence of autoregulation from gene expression data. This method only needs to compare the mean and variance of the gene expression level. Compared to other methods for inferring autoregulation, our method only requires non-interventional one-time data, and does not need to estimate parameters. Besides, our method has few restrictions on the model. We apply this method to four groups of experimental data and find some genes that might have autoregulation. Some inferred autoregulations have been verified by experiments or other theoretical works.

A new bearing fault diagnosis method via simulation data driving transfer learning without target fault data

Transfer learning exhibits exciting advantages in solving the data shortage in fault diagnosis, while most of the existing methods still need target domain fault data, which weakens the performance in some applications where the target fault data could not be provided. Focusing on the no fault data problems, this paper proposes a new transfer learning method based on simulation data. During the route of the proposed method, the theoretical fault characteristic frequencies are pre-evaluated for the monitored bearing, based on which the fault impulses are then constructed. The fault vibration signals are further simulated via mixing the constructed fault impulses with the measured normal baseline data. The envelope spectra of the simulation signals are used as the input to train a network with multi-head attention to identify fault types of the target bearing. The diagnosis performance of the proposed method has been validated via three groups of experimental data.

Bearing fault diagnosis method based on complete center frequency distribution feature

Considering the difficulty of selecting sensitive fault features in bearing health diagnosis, a fault diagnosis method based on complete center frequency distribution feature (CCFDF) is proposed. By making full use of the sensitivity of the center frequency to the signal spectrum distribution in variational mode decomposition (VMD) and extracting the complete distribution feature of the center frequency under different parameter combinations, CCFDF can effectively characterize the difference in bearing vibration signals under different health conditions and avoid the parameter setting problem in VMD. Finally, two groups of experimental data are used to verify the effectiveness of the method, and the recognition accuracy was 99% and 97%, respectively. Therefore, this method can effectively characterize the difference characteristics of different signals and achieve the final bearing fault diagnosis.

Effects of Composite Electrode Structure on Performance of Intermediate-Temperature Solid Oxide Electrolysis Cell

The composite electrode structure plays an important role in the optimization of performance of the intermediate-temperature solid oxide electrolysis cell (IT-SOEC). However, the structural influence of the composite electrode on the performance of IT-SOEC is not clear. In this study, we developed a three-dimensional macroscale model coupled with the mesoscale model based on percolation theory. We describe the electrode structure on a mesoscopic scale, looking at the electrochemical reactions, flow, and mass transport inside an IT-SOEC unit with a composite electrode. The accuracy of this multi-scale model was verified by two groups of experimental data. We investigated the effects of operating pressure, volume fraction of the electrode phase, and particle diameter in the composite electrode on electrolysis reaction rate, overpotential, convection/diffusion flux, and hydrogen mole fraction. The results showed that the variation in the volume fraction of the electrode phase had opposite effects on the electrochemical reaction rate and multi-component diffusion inside the composite electrode. Meanwhile, an optimal range of 0.8–1 for the particle diameter ratio was favorable for hydrogen production. The analysis of IT-SOEC with composite electrodes using this multi-scale model enables the subsequent optimization of cell performance and composite electrode structure.

Time series prediction of hydrate dynamics on flow assurance using PCA and Recurrent neural networks with iterative transfer learning

Preventing gas hydrate formation is critical in offshore gas and oil production systems. Several models can predict hydrate formation, however, these empirical approaches have limitations due to dependency on geometries and fluid characteristics of the systems. The trends of hydrate formation or risk are considered statistical, which means there is no definite model to describe its behavior. Herein, we present a novel framework based on a combination of feature reduction methods and several deep learning models to predict the hydrate formation trend through the multivariate sensor data. Transition and segregation trends during hydrate formation were predicted in real-time using sequential time series data from the last 60 s. We employed various deep learning models (Dense, LSTM, GRU, ARLSTM), layers, and dropout to investigate and enhance the prediction ability of each model. Two groups of experimental data (200 rpm, 600 rpm) were used for training and testing the prediction to examine the universal applicability of the model. Transfer learning in training the model was employed to apply the discrete experimental set into time-series data and enhance the accuracy. The results with higher layer numbers and a dropout rate of 0.2 ∼ 0.6 showed the best performance. ARLSTM showed the smallest error among deep learning models and predicted the good trend of kinetic characteristics (transition and segregation part) during the hydrate formation. This approach based on deep learning can be adopted for risk and issue detection of pipelines in the gas production system.

Coupled electromagnetic heat transfer model for cemented tailings backfill cured by microwave energy

Cemented tailings backfill (CTB) is a composite material containing binder, tailings and water, and it is commonly made available for filling mine-out areas and managing waste tailings. When backfilled, the CTB strength and its gain rate should be guaranteed for safe and efficient mining. It is acknowledged that increasing the CTB temperature is beneficial to its strength development. For this reason, microwave heating is introduced and applied to raise the CTB temperature quickly and thus accelerate its strength gain. To date, no numerical research has been conducted to investigate the electromagnetic and thermal properties of microwave cured CTB. Therefore, the present study proposes a coupled electromagnetic heat transfer model for the CTB heated by microwave energy. This proposed model considers the conversion of energy (electrical to electromagnetic and then to thermal), courses of heat conduction and convection, and relevant temperature variation. Two groups of experimental data are used to verify the prediction ability of the model simulation, and this model can accurately describe the thermal properties of the microwave heated CTB. Based on the validated model, the effects of input power, frequency and sample dimension on the distribution of heat and electromagnetic field are discussed. This study can be a contribution to the optimization of microwave parameters and sample dimension, for higher temperature development, less energy consumption and more uniform heating, in future laboratory experiments and field applications.

Calibration of camera internal parameters based on grey wolf optimization improved by levy flight and mutation

Traditional calibration technology has been widely used in measurement and monitoring; however, there are limitations of poor calibration accuracy, which can not meet the accuracy requirements in some scenarios. About this problem, we proposed a grey wolf optimization algorithm based on levy flight and mutation mechanism to solve camera internal parameters in this paper. The algorithm is based on the actual nonlinear model, which takes the minimum average value of reprojection error as the objective function. The grey wolf position is randomly generated within a given range. Then, the grey wolf optimization algorithm based on levy flight and mutation mechanism is used to iteratively calculate the optimal position, which is the internal parameters of cameras. The two groups of experimental data were performed to verify the algorithm. The result shows better effectiveness and calibration accuracy of the proposed algorithm compared with other optimization methods.

Comparative study of data from geotechnical field penetration tests on sandy subsoils

The main objective of the work reported in this paper was to compare standard penetration test (SPT), dynamic probing (DP) and cone penetration test (CPT) data for uniformly graded sandy soils located in the southeast Brazilian coastal region of Grande Vitória, Espírito Santo (GV-ES). Previously reported datasets of laboratory and field penetration test data concerning the variations of some soil parameters associated with the investigated correlations were also evaluated. The correlations analysis addressed the influences of textural characteristics, especially grain size distribution, and relative density on the obtained CPT–SPT and DP–SPT linear correlations using a newly compiled experimental database for GV-ES sands and the previously published datasets. Correlations were determined using statistical inference, specifically by simple linear regression and analysis of residues for several groups of experimental data.

An approach for learning resource recommendation using deep matrix factorization

ABSTRACT In traditional learning, learners and their lecturers, or tutors can meet face-to-face. In such lectures, the lecturers, or tutors can introduce printed book tutorials. However, in several circumstances, such as distance education, learners cannot interact with their teachers. Therefore, online learning resources would be helpful for learners to get knowledge. With a large and diverse number of learning resources, selecting appropriate learning resources to learn is very important. This study presents a deep matrix decomposition model extended from standard matrix decomposition to recommend learning resources based on learners' abilities and requirements. We test the proposed model on two groups of experimental data, including the data group of students' learning outcomes at a university for course recommendation and another group of 5 datasets of user learning resources to provide valuable recommendations for supporting learners. The experiments have revealed promising results compared to some baselines. The work is expected to be a good choice for large-scale datasets.

Multi-objective optimization of an integrated biomass waste fixed-bed gasification system for power and biochar co-production

In this work, an integrated biomass waste gasification combined heat and power (CHP) system is simulated and analyzed based on a multi-scale first-principles numerical model, which consists of a 1D fixed-bed gasifier model and a 1D hybrid peripheral fragmentation and shrinking-core biomass particle model. The simulations are based on a 15kW gasification CHP system and the numerical model is validated by two different groups of experimental data, and the maximum deviation is 2.56% in syngas H2 composition prediction. A multi-objective optimization framework of the integrated biomass waste gasification CHP system is formulated that considers 3 objectives: economic benefit, greenhouse gas emission, and potential health impact caused by PM emission. The best-compromised solution from the Pareto front leads to $142.8/day economic benefit, 694.3 kg CO2eq/day mitigation, and 57.67 Singapore Pollutant Standards Index score. The impacts of the three objectives on decision variables (i.e., feedstock flowrate, feedstock moisture content, and equivalent ratio) are analyzed by investigating 21 different weighted single-objective problems.

Prediction of cleaned coal yield based on different S-shaped curve models in coal cleaning production

Five S-shaped curve models are proposed to accurately predict the product yield and reduce the waste of precious coal resources during the coal cleaning process. Taking the Hill model (HM) as an example, the derivation process of parameters is described. The model’s accuracy is then verified by calculating the standard deviation, summary statistics, and residual plots for six groups of experimental data. It shows that the optimal prediction models for samples 1 to 6 are HM, the Gompertz model (GM), GM, the Logistic model (LM), the arctangent model (AM), and the normal integral model (NIM), respectively. The mean standard deviations of HM, GM, LM, NIM, and AM are 2.21, 2.23, 2.37, 2.41, and 3.06, respectively, indicating that the prediction accuracy of the five models is also arranged in this order. The prediction results of the optimal model (GM) are then verified by an industrial test in the Liudong Coal Cleaning Plant. The absolute errors of the separation density, cleaned coal yield, and cleaned coal ash are 0.005kg/L, 0.46%, and -0.09%, respectively. The maximum absolute error of the partition coefficients predicted by GM is -2.89%, while the maximum absolute error predicted by NIM alone is 8.19%, which is 5.30% higher than that predicted by GM. Furthermore, the error of the predicted partition coefficients near the separation density is usually greater than that at both ends of the partition curve, which is acceptable and typical. This work demonstrates that the prediction of cleaned coal yield based on different S-shaped curve models in the coal cleaning process is feasible, efficient, economic, and eco-friendly, and it has potential industrial application.

Proton exchange membrane fuel cell degradation model based on catalyst transformation theory

AbstractDurability is a severe problem in the commercial application of proton exchange membrane fuel cell (PEMFC). The platinum (Pt) catalyst which greatly affects performance and durability is one of the most important components in PEMFC. However, few PEMFC degradation models focused on the Pt catalyst degradation process in molecule scale. In this paper, a novel PEMFC catalyst degradation model is developed based on the catalyst transformation theory. In this model, the Pt catalyst degrades by changing form and size according to the transformation theory, which includes Pt dissolution and Ostwald ripening mechanisms. The advantage of this new model is that the transformation of every Pt particle is calculated directly, and the characteristics of every particle are considered, thus it can give more accurate catalyst degradation process. Six groups of experimental data have been used to validate the effectiveness of the proposed degradation model of PEMFC. Furthermore, the accuracy of the proposed degradation model is superior to a traditional model, and the application of such a degradation model to the performance prediction of the PEMFC has been discussed.

Early fault diagnosis of rolling bearings based on signal reconstruction through SDD-SVD and FWEO

An approach based on the singular spectrum decomposition (SSD)-singular value decomposition (SVD) and frequency weight energy operator (FWEO) was proposed for early fault diagnosis of rolling bearings. Since the interference of heavy noise in the early stage of bearing fault, SSD could eliminate abundant noise, meanwhile adaptively decompose the nonlinear, non-stationary signals into multiple mono-components which had distinct physical meanings. Subsequently, SVD was adopted for de-noising in every mono-components because the noise was distributed throughout the frequency domain. In the following, signal reconstruction was performed based on the selected mono-components with de-noising. Finally, FWEO was employed for envelop analysis and the early fault diagnosis of the rolling bearing is realized. Two groups of experiment data concerning signals of early bearing fault were investigated to valid the effectiveness of the proposed method, and good fault identification effects were obtained.

Managing the Effectiveness of Teaching Boys Aged 10 Mawashi-Geri (Roundhouse Kick) Technique in Kyokushin Karate

The objective of the study was to substantiate the influence of selected independent variables for building optimal models of teaching a series of tasks aimed at motor skills development in boys aged 10. Materials and methods. The study involved 32 boys aged 10. The children and their parents were informed about all the features of the study and gave their consent to participate in the experiment. To achieve the objective set, the following research methods were used: study and analysis of scientific and methodological literature; pedagogical observation, timing of learning tasks; pedagogical experiment, methods of mathematical statistics, methods of mathematical experiment planning. In the process of teaching, a method of algorithmic instructions was used. Results. The effect size of the factor varied from series to series. The calculated Cochran's criterion showed that all the groups of experimental data were obtained from the same population. It was found that the variances do not differ for the selected significance level of 0.05. Each independent variable (X1, X2, X1X2) plays a role in changing the indicator of proficiency in the Mawashi-geri-jodan (roundhouse kick). The constructed mathematical models adequately describe the obtained data. The calculated regression coefficients are statistically significant (Fр < Fkр), which means that the regression model is effective. Throughout the experiment, the independent variable Х1 “number of sets” had a constant positive effect on the target feature (Y). The percentage contribution in the first series of learning tasks was 59.3%, in the second – 88.6%, in the third – 56%, in the fourth – 93.9%, in the fifth – 81.3%, in the sixth – 69.62%. The independent variable Х2 “rest interval” has a negative effect in the first series – 25.9%, in the second series – 10.6%, in the fifth – 9.9%, in the sixth – 13.1%. A positive effect of the first-order interaction (X1X2) was observed in the third series – 29.1%, the fifth – 8.9%, the sixth – 17.28%. Conclusions. The optimal combinations of conditions for the series of tasks when teaching boys aged 10 the Mawashi-geri-jodan (roundhouse kick) are the following: series 1 – 3 sets, rest interval 60 s; series 2 – 3 sets, rest interval 60 s; series 3 – 3 sets, rest interval 120 s; series 4 – 3 sets, rest interval 60–120 s; series 5 – 3 sets, rest interval 120 s; series 6 – 3 sets, rest interval 120 s.

Health Monitoring of IGBTs with a Rule-Based Sub-safety Recognition Model Using Neural Networks

IGBTs are used everywhere ranging from aerospace, to transportation systems to the grid but it’s the most fragile device in power electronics. So it’s very critical to evaluate the health state and take advanced and active maintenance measures to avoid the accidents. This paper develops a rule-based sub-safety recognition model using neural networks to evaluate the degradation degree of the IGBTs and determine the health state. The model was validated with two groups of experimental data.

A wave propagation model with the Biot and the fractional viscoelastic mechanisms

Energy loss in porous media containing fluids is typically caused by a variety of dynamic mechanisms. In the Biot theory, energy loss only includes the frictional dissipation between the solid phase and the fluid phase, resulting in underestimation of the dispersion and attenuation of the waves in the low frequency range. To develop a dynamic model that can predict the high dispersion and strong attenuation of waves at the seismic band, we introduce viscoelasticity into the Biot model and use fractional derivatives to describe the viscoelastic mechanism, and finally propose a new wave propagation model. Unlike the Biot model, the proposed model includes the intrinsic dissipation of the solid frame. We investigate the effects of the fractional order parameters on the dispersion and attenuation of the P- and S-waves using several numerical experiments. Furthermore, we use several groups of experimental data from different fluid-saturated rocks to testify the validity of the new model. The results demonstrate that the new model provides more accurate predictions of high dispersion and strong attenuation of different waves in the low frequency range.