Experimental Study on Local Scour Depth around Monopile Foundation in Combined Waves and Current

Local scour is one of the key factors that cause the collapse of structures. To avoid structure failures and economic losses in water, it is usually essential to predict the equilibrium scour depth of the foundation. In this study, several design models which were presented to predict the equilibrium scour depth either under steady clear water conditions and combined waves and current conditions were recommended. These models from China, the United States and Norway were analyzed and compared through experiments. Moreover, flume tests for monopile foundation embedded in sand under different flow conditions were carried out to observe the process and gauge the maximum depth around the pile. Based on this study, for predicting the equilibrium scour depth around bridge piers, the computational results of three design methods are all conservative, as expected. For the foundation of offshore structures in marine environment, most of the predicted scour depths by design methods are different from field data; in particular, the mean relative error with these design methods proposed may reach up to 966.5%, which may lead to underestimation of the problem, overdesign and consequently high construction cost. To further improve the ability of the scour prediction in a marine environment, data from flume tests and some field data from a previous study were used to derive the major factors of scour. Based on the dimensional analysis method, a new model to estimate the equilibrium scour depth induced by either current or waves is proposed. The mean relative error of the new formula is 49.1%, and it gives more accurate scour depth predictions than the existing methods.

Reconstruction of Lamb Wave Dispersion Curves in Different Objects Using Signals Measured at Two Different Distances

The possibilities of an effective method of two adjacent signals are investigated for the evaluation of Lamb waves phase velocity dispersion in objects of different types, namely polyvinyl chloride (PVC) film and wind turbine blade (WTB). A new algorithm based on peaks of spectrum magnitude is presented and used for the comparison of the results. To use the presented method, the wavelength-dependent parameter is proposed to determine the optimal distance range, which is necessary in selecting two signals for analysis. It is determined that, in the range of 0.17–0.5 wavelength where δcph is not higher than 5%, it is appropriate to use in the case of an A0 mode in PVC film sample. The smallest error of 1.2%, in the distance greater than 1.5 wavelengths, is obtained in the case of the S0 mode. Using the method of two signals analysis for PVC sample, the phase velocity dispersion curve of the A0 mode is reconstructed using selected distances x1 = 70 mm and x2 = 70.5 mm between two spatial positions of a receiving transducer with a mean relative error δcph=2.8%, and for S0 mode, x1 = 61 mm and x2 = 79.7 mm with δcph=0.99%. In the case of the WTB sample, the range of 0.1–0.39 wavelength, where δcph is not higher than 3%, is determined as the optimal distance range between two adjacent signals. The phase velocity dispersion curve of the A0 mode is reconstructed in two frequency ranges: first, using selected distances x1 = 225 mm and x2 = 231 mm with mean relative error δcph=0.3%; and second, x1 = 225 mm and x2 = 237 mm with δcph=1.3%.

Relationship between photosynthetically active radiation and global radiation in Petrolina and Brasília, Brazil

ABSTRACT Photosynthetically active radiation (PAR) comprises the spectral range of global solar radiation (Rs) that is highly related to vegetation productivity. The study aimed to evaluate the relationship between PAR and Rs in Petrolina, PE, and Brasília, DF, Brazil, with data measured in 2011 and 2013 at two stations of the Sistema Nacional de Organização de Dados Ambientais located in Petrolina, PE and Brasília, DF, Brazil, and the obtained models were evaluated using the measurements of 2014. It was verified that the PAR, in instantaneous values (μmol m-2 s-1), can be estimated at 2.31 times the Rs (W m-2) measured in Petrolina, while for daily values of PAR (MJ m-2) is equal to 50% of Rs (MJ m-2). In Brasília, PAR (μmol m-2 s-1) is 2.05 times the Rs (W m-2) and, in daily values, equal to 44% of Rs (MJ m-2). The variability of the PAR/Rs ratio followed the local variations of clearness index (Kt) and Rs. The models presented an adequate performance based on statistical indices mean absolute error, mean relative error, and root mean square error and can be used to estimate PAR.

Research on Management Model Based on Deep Learning

In this study, the focus was on the development of management models and future prediction for the cost and risk by using an improved deep learning (DL) algorithm. Management model can be defined as the management activities that are interlinked and organized inside organization of institutions. Different opportunities and different organizations are offered by different management models. Proper management models lead to strategies and decisions help to success organization. Deep neural network (DNN) is proposed to make good prediction for organization for increasing the cost and reduce risk in companies and institutions. The error of prediction is updated according to variable hidden layers and nodes within iteration. Improved DNN is used and modify weights that have an effect on the features extracted in advance to increase the accuracy and precisions are used. The proposed method is based on dynamic hidden layers with backpropagation and feedforward. Absolute mean relative error (AMRE) and variance (R2) are used for evaluation in term of accuracy. The training system is used with three available datasets from big company, health issue, and industry. Gained result proves the worth of the proposed system and is suitable for predicting complex data and reducing the risk as possible.

An adaptive echo attenuation correction method for airborne Ka-band precipitation cloud radar based on melting layer

In this study, an airborne Ka-band Precipitation Cloud Radar (KPR) is used to carry out a cloud observation experiment.By analyzing the attenuation of the snow echo, it is found that during the snowfall, due to the low liquid water content, the KPR attenuation is small on the detection path, and after preliminary comparative analysis, the maximum attenuation correction value is 0.5 dBZ. According to the echo attenuation analysis of mixed precipitation, the melting layer is found to be the key factor affecting the attenuation correction. This study hereby proposes an adaptive echo attenuation correction method based on the melting layer (AEC), and uses the ground-based S-band radar to extract the echo on the aircraft trajectory to verify the correction results. The results show that the echo attenuation correction value above the melting layer is related to the flight position. The aircraft above the melting layer is dominated by ice particles, with small attenuation correction value, the maximum correction amount of 0.13 dBZ; when the aircraft is at and just below the melting layer, a water film is prone to be on the antenna, which leads to serious attenuation of the KPR detection path, with the attenuation correction value 1~2 dBZ. For the precipitation echo below the melting layer, due to the abundant rain and water vapor content, the KPR attenuation is significant with maximum correction value of about 5 dBZ. Compared with the S-band radar, before attenuation correction, the total mean relative error is 15 %, and the correlation coefficient is 0.82; after correction, the total mean relative error is 6 %, and the correlation coefficient is 0.90, indicating the significant improvement of the KPR data quality.

Evaluation of a Quasi-steady state approximation of the cloud Droplet Growth Equation (QDGE) scheme for aerosol activation in global models using multiple aircraft data over both continental and marine environments

This research introduces a numerically efficient aerosol activation scheme and evaluates it by using stratus and stratocumulus cloud data sampled during multiple aircraft campaigns in Canada, Chile, Brazil, and China. The scheme employs a Quasi-steady state approximation of the cloud Droplet Growth Equation (QDGE) to efficiently simulate aerosol activation, the vertical profile of supersaturation, and the activated cloud droplet number concentration (CDNC) near the cloud base. We evaluate the QDGE scheme by specifying observed environmental thermodynamic variables and aerosol information from 31 cloud cases as input and comparing the simulated CDNC with cloud observations. The average of mean relative error of the simulated CDNC for cloud cases in each campaign ranges from 17.30 % in Brazil to 25.90 % in China, indicating that the QDGE scheme successfully reproduces observed variations in CDNC over a wide range of different meteorological conditions and aerosol regimes. Additionally, we carried out an error analysis by calculating the Maximum Information Coefficient (MIC) between the mean relative error (MRE) and input variables for the individual campaigns and all cloud cases. MIC values are then sorted by aerosol properties, pollution level, environmental humidity, and dynamic condition according to their relative importance to MRE . Based on the error analysis we found that the magnitude of MRE is more relevant to the specification of input aerosol pollution level in marine regions and aerosol hygroscopicity in continental regions than to other variables in the simulation.

Estimation of Isentropic Compressibility of Biodiesel Using ELM Strategy: Application in Biofuel Production Processes

Isentropic compressibility is one of the significant properties of biofuel. On the other hand, the complexity related to the experimental procedure makes the detection process of this parameter time-consuming and hard. Thus, we propose a new Machine Learning (ML) method based on Extreme Learning Machine (ELM) to model this important value. A real database containing 483 actual datasets is compared with the outputs predicted by the ELM model. The results of this comparison show that this ML method, with a mean relative error of 0.19 and R 2 values of 1, has a great performance in calculations related to the biodiesel field. In addition, sensitivity analysis exhibits that the most efficient parameter of input variables is the normal melting point to determine isentropic compressibility.

On the Investigation of Effective Factors on Higher Heating Value of Biodiesel: Robust Modeling and Data Assessments

Higher heating value (HHV) is one of the properties of biomass fuels which is essential in investigating their special characteristics and potentialities. In this paper, various techniques based on Gaussian process regression (GPR) were utilized to assess this value for biomass fuels, including several kernel functions, i.e., exponential, Matern, rational quadratic, and squared exponential. An extensive databank was collected from literature. The findings were compared, and the results indicated that Exponential-based model was more accurate, with the coefficient of regression ( R 2 ) of 0.961 and the mean relative error (% MRE) of 3.11 for total data. Compared to former models presented by previous researchers, the model proposed in this study showed a higher ability to predict output values. With various analyses, it can be concluded that the proposed method has a high rate of efficiency in assessing the HHV of various biomass.

P–105 Clinical validation of mojo AISA, an artificial intelligence robotic CASA system

Study question Can a CASA system based on Artificial Intelligence perform as well as manual semen assessment, within the WHO error margins? Summary answer The AI-based CASA systems that mimic high quality assessments show great potential for reducing clinical workloads while increasing treatment efficacy. What is known already The field of male-factor fertility investigation is still lacking an automated semen analysis system that can be widely clinically adopted. By leveraging state-of-the-art robotics and Artificial Intelligence (AI), it was possible to build mojo AISA which is an AI and robotic platform designed according to WHO recommendation for semen analysis. This system is based on AI software with a unique convolutional neural network (CNN) that detects and measures sperm concentration and motility while ruling out unwanted cells and debris in raw samples. Study design, size, duration This study presents and validates the mojo AISA device. A total of 60 patient samples at ANOVA Karolinska University Hospital were collected and results from manual assessment were compared to mojo AISA for concentration and motility. Semen samples were assessed manually (WHO 2010) and concurrently with Mojo AISA. Manual measurements ranged from 1–206M/ml. This study lasted from May 2020 to December 2020 following informed consent and ethics committee practices of ANOVA. Participants/materials, setting, methods Sample preparation protocol for mojo AISA consisted of placing two 10ul drops and covering with two 22x22mm coverslip. Manual assessment followed ANOVA EQA procedures akin to the WHO. A CNN was trained using videos captured with mojo AISA as input data. Images were annotated to form a validation set by which the AI was trained. To account for sampling error, videos of Hamilton Thorne Accubeads+ were captured using mojo AISA and the mojo counting chambers. Main results and the role of chance Comparing the concentration measured by mojo AISA with the known value for each microbead, results are in agreement of 86%, within the confidence interval of the microbeads. The mean relative error was 6.7% and maximum error was 11%. Therefore, Accubeads+ validation proved no observational error regarding the use of mojo AISA microscope. As for comparing mojo AISA to manual assessment for concentration, Pearson (Spearman) correlation was 0.95 (0.97). The mean relative error was 24.8% and maximum relative error was 71.1%, where 90% of samples were below 50% error. By looking at the concentration range between 10 and 20 M/ml, mojo AISA displayed a mean error of 18.5%. For motility, as comparing mojo AISA to manual assessment, a result of 35.4% mean relative error was obtained. To conclude, mojo’s robotic solution shows promise for clinical practice as the AI continues to improve. In 6 months, sperm concentration correlation improved by 3-fold. Next, the AI will be further clinically trained for low concentration. Limitations, reasons for caution mojo AISA requires further development, especially for very low concentration ranges, below 5M/ml, due to high sensibility to false positive detections. The same applies to post-vasectomy samples. Additionally, the necessity to compute the motility of each sperm scales poorly with high concentration generating a poor experience for high volume clinics. Wider implications of the findings: Automation is crucial in several industries. It enables fertility clinics & andrologists to standardize male factor infertility measurements (if paired with widespread standardization of protocols for automation) while enabling them to put more focus on demanding activities of their profession and removes human biases of inter-laboratory performance. Trial registration number Not applicable

Pool Boiling Heat Transfer Coefficients in Mixtures of Water and Glycerin

Heat transfer coefficients were investigated for saturated nucleate pool boiling of binary mixtures of water and glycerin at atmospheric pressure in a wide range of concentrations and heat fluxes. Mixtures with water mass fractions from 100% to 40% were boiled on a horizontal flat copper surface at heat fluxes from about 25 up to 270kWm−2. Experiments were carried out by static and dynamic method of measurement. Results of the static method show that the impact of mixture effects on heat transfer coefficient cannot be neglected and ideal heat transfer coefficient has to be corrected for all investigated concentrations and heat fluxes. Experimental data are correlated with the empirical correlation α=0.59q0.714+0.130ωw with mean relative error of 6%. Taking mixture effects into account, data are also successfully correlated with the combination of Stephan and Abdelsalam (1980) and Schlünder (1982) correlations with mean relative error of about 15%. Recommended coefficients of Schlünder correlation C0=1 and βL=2×10−4ms−1 were found to be acceptable for all investigated mixtures. The dynamic method was developed for fast measurement of heat transfer coefficients at continuous change of composition of boiling mixture. The dynamic method was tested for water–glycerin mixtures with water mass fractions from 70% down to 35%. Results of the dynamic method were found to be comparable with the static method. For water–glycerin mixtures with higher water mass fractions, precise temperature measurements are needed.