Optimization of the Performance of Marine Diesel Engines to Minimize the Formation of SOx Emissions

Optimization procedures are required to minimize the amount of fuel consumption and exhaust emissions from marine engines. This study discusses the procedures to optimize the performance of any marine engine implemented in a 0D/1D numerical model in order to achieve lower values of exhaust emissions. From that point, an extension of previous simulation researches is presented to calculate the amount of SOx emissions from two marine diesel engines along their load diagrams based on the percentage of sulfur in the marine fuel used. The variations of SOx emissions are computed in g/kW·h and in parts per million (ppm) as functions of the optimized parameters: brake specific fuel consumption and the amount of air-fuel ratio respectively. Then, a surrogate model-based response surface methodology is used to generate polynomial equations to estimate the amount of SOx emissions as functions of engine speed and load. These developed non-dimensional equations can be further used directly to assess the value of SOx emissions for different percentages of sulfur of the selected or similar engines to be used in different marine applications.

Data Modeling Using Finite Differences

The Common Core State Standards for Mathematics (CCSSM) states that high school students should be able to recognize patterns of growth in linear, quadratic, and exponential functions and construct such functions from tables of data (CCSSI 2010). Accordingly, many high school curricula include a method that uses finite differences between data points to generate polynomial functions. That is, students may examine differences between successive output values (called first differences), successive differences of the first differences (second differences), or successive differences of the (n - 1)th differences (nth-order differences), and rely on the following:

A Postprocessor for Generating Multi-Axis Polynomial Interpolation NC Machining Program

Multi-axis CNC machining shows great advantages in manufacturing parts with geometries being defined by complex surfaces. NURBS (Non-Uniform Rational B-Spline) interpolation and polynomial interpolation are two new types of interpolation to improve productivity. Compared to the NURBS interpolation, the polynomial interpolation can gain higher productivity. However, no commercial software tool is available to generate polynomial interpolation CNC programs. This paper proposes a postprocessor to generate polynomial interpolation multi-axis CNC program. A case study with simulations demonstrated that the polynomial interpolation program generated by the proposed postprocessor has higher machining efficiency than liner interpolation and NURBS interpolation program.

Hierarchical Function Approximation with a Neural Network Model

This article presents a neural network model that permits to build a conceptual hierarchy to approximate functions over a given interval. Bio-inspired axo-axonic connections are used. In these connections the signal weight between two neurons is computed by the output of other neuron. Such arquitecture can generate polynomial expressions with lineal activation functions. This network can approximate any pattern set with a polynomial equation. This neural system classifies an input pattern as an element belonging to a category that the system has, until an exhaustive classification is obtained. The proposed model is not a hierarchy of neural networks, it establishes relationships among all the different neural networks in order to propagate the activation. Each neural network is in charge of the input pattern recognition to any prototyped category, and also in charge of transmitting the activation to other neural networks to be able to continue with the approximation.