Predicting the Thickness of the Hardening Coating during Diffusion Metallization of Cast Iron

The article presents the calculations of diffusion indices of saturation of high-strength cast iron VCh 60 from powder filling. Carbide-forming elements were used as diffusers: vanadium, chromium and manganese. As a result of the research, empirical equations have been established for predicting the thickness of strengthening diffusion coatings depending on the temperature and saturation time.

Data-Driven Modeling of Ultimate Load Capacity of Closed- and Open-Ended Piles using Machine Learning

Field pile load tests are fairly expensive experiments that can be applied to certain pile types required to be installed in full scale. Hence, it is neither practical nor efficient to perform a load test for every installed pile. While there exist many empirical relations for predicting pile capacities, such methods typically suffer from accuracy and generality. Therefore, current geotechnical practice still looks for methods to accommodate full-scale pile load testing to serve as both accurate and practical tools. In this study, load bearing capacities of closed and open-ended piles in cohesive and cohesionless soils are predicted using machine learning. Nine such methods are utilized in the analyses where CPT and pile data are considered as the learning features necessary to teach those methods the database gathered via a comprehensive search. Then, machine learning models are developed, and the databases are separated into five-folds according to the cross-validation-principle, which are used for both training and testing of the machine learning methods. Model predictions are validated with classical empirical equations. Results indicate that the Relevance Vector Regression and the Random Forest methods typically generate considerably better predictions than the other methods and empirical equations. Hence, machine learning methods are found as reliable tools to predict the pile load capacities of both open-ended and closed-ended piles provided that there is a large enough database and an appropriate method to use.

Estimation of Wave-Breaking Index by Learning Nonlinear Relation Using Multilayer Neural Network

Estimating wave-breaking indexes such as wave height and water depth is essential to understanding the location and scale of the breaking wave. Therefore, numerous wave-flume laboratory experiments have been conducted to develop empirical wave-breaking formulas. However, the nonlinearity between the parameters has not been fully incorporated into the empirical equations. Thus, this study proposes a multilayer neural network utilizing the nonlinear activation function and backpropagation to extract nonlinear relationships. Existing laboratory experiment data for the monochromatic regular wave are used to train the proposed network. Specifically, the bottom slope, deep-water wave height and wave period are plugged in as the input values that simultaneously estimate the breaking-wave height and wave-breaking location. Typical empirical equations employ deep-water wave height and length as input variables to predict the breaking-wave height and water depth. A newly proposed model directly utilizes breaking-wave height and water depth without nondimensionalization. Thus, the applicability can be significantly improved. The estimated wave-breaking index is statistically verified using the bias, root-mean-square errors, and Pearson correlation coefficient. The performance of the proposed model is better than existing breaking-wave-index formulas as well as having robust applicability to laboratory experiment conditions, such as wave condition, bottom slope, and experimental scale.

210Pb-Derived Bioturbation Rates in Sediments Around Seamounts in the Tropical Northwest Pacific

To evaluate bioturbation coefficients (DB) and mixing depths (L), 210Pb and 226Ra activity was measured in two sediments cores (from water depths of 5,398 m and 4,428 m), which were collected from seamount areas in the Northwest Pacific. Using a steady-state diffusion mode, we estimated DB values of 16.8 and 24.1 cm2/a, higher than those in abyssal sediments and those predicted by traditional empirical equations. Corresponding L values varied between 19.3 and 23.1 cm. These high values indicate that seamounts are the area of active bioturbation. A one-dimensional model for the transport of total organic carbon (TOC) from the surface layer of sediments to the deep layer was developed using the distribution pattern of the specific activity of excess 210Pb (210Pbex) and its relationship with TOC. The model showed that the TOC flux transmitted downward by bioturbation was 0.09 mmol/(cm2⋅a) and 0.12 mmol/(cm2⋅a).

DEVELOPMENT OF A MATHEMATICAL MODEL FOR PERFORMANCE PREDICTION OF PLANING CATAMARAN IN CALM WATER

In this paper, an attempt has been made to predict the performance of a planing catamaran using a mathematical model. Catamarans subjected to a common hydrodynamic lift, have an extra lift between the two asymmetric half bodies. In order to develop a mathematical model for performance prediction of planing catamarans, existing formulas for hydrodynamic lift calculation must be modified. Existing empirical and semi-empirical equations in the literature have been implemented and compared against available experimental data. Evaluation of lift in comparison with experimental data has been documented. Parameters influencing the interaction between demi-hulls and separation effects have been analyzed. The mathematical model for planing catamarans has been developed based on Savitsky’s method and results have been compared against experimental data. Finally, the effects of variation in hull geometry such as deadrise angle and distance between two half bodies on equilibrium trim angle, resistance and wetted surface have been examined.